Quantification and expert evaluation of evidence for chemopredictive biomarkers to personalize cancer treatment.

Predictive biomarkers have the potential to facilitate cancer precision medicine by guiding the optimal choice of therapies for patients. However, clinicians are faced with an enormous volume of often-contradictory evidence regarding the therapeutic context of chemopredictive biomarkers.We extensively surveyed public literature to systematically review the predictive effect of 7 biomarkers claimed to predict response to various chemotherapy drugs: ERCC1-platinums, RRM1-gemcitabine, TYMS-5-fluorouracil/Capecitabine, TUBB3-taxanes, MGMT-temozolomide, TOP1-irinotecan/topotecan, and TOP2A-anthracyclines. We focused on studies that investigated changes in gene or protein expression as predictors of drug sensitivity or resistance. We considered an evidence framework that ranked studies from high level I evidence for randomized controlled trials to low level IV evidence for pre-clinical studies and patient case studies.We found that further in-depth analysis will be required to explore methodological issues, inconsistencies between studies, and tumor specific effects present even within high evidence level studies. Some of these nuances will lend themselves to automation, others will require manual curation. However, the comprehensive cataloging and analysis of dispersed public data utilizing an evidence framework provides a high level perspective on clinical actionability of these protein biomarkers. This framework and perspective will ultimately facilitate clinical trial design as well as therapeutic decision-making for individual patients

ICE (Intensive Community Empowerment) Sebagai Solusi Upaya Mencegah Kenaikan Angka Kematian Ibu (AKI) Sebagai Program Percontohan Di Wilayah Kelurahan Bangetayu Wetan Kecematan Genuk Kota Semarang

Kesehatan ibu merupakan target dalam Millenium Development Goals (MDGs). Angka kematian ibu (AKI) merupakan salah satu indikator dalam menentukan derajat kesehatan masyarakat. Angka kematian ibu di Indonesia tertinggi dibandingkan dengan negara ASEAN lainnya. Menurunkan angka kematian ibu dengan cara yang kreatif perlu dilakukan agar kematian pada ibu menurun. ICE (intensive commmunity empowerment) dengan langkah-langkah; Mapping Strategy, Penyuluhan Intensif, dan Pemberdayaan Dukun Bersalin yang merupakan program inovatif untuk menurunkan kematian ibu di Kelurahan Bangetayu Wetan Kecamatan Genuk Kota Semarang. Kecamatan Genuk merupakan kecamatan yang 19 kasus dari 25.746 jumlah kelahiran hidup atau sekitar 73,80 per 100.000 KH. Sasaran program ini adalah pasangan usia subur dan dukun beranak. Tahapan pelaksanaan program ini dimulai dengan mapping strategy, penyuluhan intensif dan pemberdayaan dukun bersalin

A pilot study evaluating concordance between blood-based and patient-matched tumor molecular testing within pancreatic cancer patients participating in the Know Your Tumor (KYT) initiative

Recent improvements in next-generation sequencing (NGS) technology have enabled detection of biomarkers in cell-free DNA in blood and may ultimately replace invasive tissue biopsies. However, a better understanding of the performance of blood-based NGS assays is needed prior to routine clinical use. As part of an IRBapproved molecular profiling registry trial of pancreatic ductal adenocarcinoma (PDA) patients, we facilitated blood-based NGS testing of 34 patients from multiple community-based and high-volume academic oncology practices. 23 of these patients also underwent traditional tumor tissue-based NGS testing. cfDNA was not detected in 9/34 (26%) patients. Overall concordance between blood and tumor tissue NGS assays was low, with only 25% sensitivity of blood-based NGS for tumor tissue NGS. Mutations in KRAS, the major PDA oncogene, were only detected in 10/34 (29%) blood samples, compared to 20/23 (87%) tumor tissue biopsies. The presence of mutations in circulating DNA was associated with reduced overall survival (54% in mutation-positive versus 90% in mutation-negative). Our results suggest that in the setting of previously treated, advanced PDA, liquid biopsies are not yet an adequate substitute for tissue biopsies. Further refinement in defining the optimal patient population and timing of blood sampling may improve the value of a blood-based test. © Pishvaian et al

Real-time cine and myocardial perfusion with treadmill exercise stress cardiovascular magnetic resonance in patients referred for stress SPECT

Background: To date, stress cardiovascular magnetic resonance (CMR) has relied on pharmacologic agents, and therefore lacked the physiologic information available only with exercise stress. Methods: 43 patients age 25 to 81 years underwent a treadmill stress test incorporating both Tc99m SPECT and CMR. After rest Tc99m SPECT imaging, patients underwent resting cine CMR. Patients then underwent in-room exercise stress using a partially modified treadmill. 12-lead ECG monitoring was performed throughout. At peak stress, Tc99m was injected and patients rapidly returned to their prior position in the magnet for post-exercise cine and perfusion imaging. The patient table was pulled out of the magnet for recovery monitoring. The patient was sent back into the magnet for recovery cine and resting perfusion followed by delayed post-gadolinium imaging. Post-CMR, patients went to the adjacent SPECT lab to complete stress nuclear imaging. Each modality's images were reviewed blinded to the other's results. Results: Patients completed on average 9.3 ± 2.4 min of the Bruce protocol. Stress cine CMR was completed in 68 ± 14 sec following termination of exercise, and stress perfusion CMR was completed in 88 ± 8 sec. Agreement between SPECT and CMR was moderate (κ = 0.58). Accuracy in eight patients who underwent coronary angiography was 7/8 for CMR and 5/8 for SPECT (p = 0.625). Follow-up at 6 months indicated freedom from cardiovascular events in 29/29 CMRnegative and 33/34 SPECT-negative patients. Conclusions: Exercise stress CMR including wall motion and perfusion is feasible in patients with suspected ischemic heart disease. Larger clinical trials are warranted based on the promising results of this pilot study to allow comparative effectiveness studies of this stress imaging system vs. other stress imaging modalities

A Comparative Study of HARR Feature Extraction and Machine Learning Algorithms for Covid-19 X-Ray Image Classification

In this study, we investigated how effectively COVID-19 image categorization using Harr feature extraction and machine learning algorithms. We were particularly interested in the effectiveness of these algorithms. A dataset of 500 X-ray scans, equally split between 250 COVID-19-positive cases and 250 healthy controls, served as the basis for our study.  K-nearest neighbors,decision tree,  Linear regression, support vector machine, regression, classification, naive Bayes,random forest,  as well as linear discriminant analysis were among the seven machine-learning approaches used to categorize the photos. With the use of Harr feature extraction, the features of the pictures were extracted. We studied the efficacy of COVID-19 X-ray images for classification utilizing the combination of machine learning as well as the Harr feature extraction methods in the present investigation due to their effectiveness. We searched a database of 500 X-rays for this investigation, dividing them equally between groups of 250 patients with COVID-19-positive cases and 250 healthy people. Following that, the images were examined using seven various machine learning approaches for recognition. These methods included naive Bayes, linear discriminant analysis, random forests, classification,k-nearest neighbors,  and regression trees. The information from the photos was gathered using the Harr feature extraction method. The effectiveness of the algorithms was evaluated with the help of a variety of metrics, such asF1 score, precision,accuracy, recall, the area under the ROC curve, and the region of interest curve. According to our research, the Support Vector Machine algorithm had the highest accuracy, at 77%, while the Naive Bayes approach had the lowest accuracy, at 58%. By using machine learning and Harr feature extraction approaches, the Random Forest method yields the best results, based on our research. The development of future COVID-19 X-ray image-based automated diagnostic systems may be influenced by these findings. Results from the suggested model were comparable to those of cutting-edge models trained using transfer learning techniques. The proposed model's main advantage is that it has ten times fewer parameters than the most advanced models.A receiver operating characteristic (ROC) curve's F1 score, and the algorithms' accuracy, precision, the area under the curve,  and recall were all used as metrics. According to our findings, the Naive Bayes method gained the least accuracy (58%) and the Support Vector Machine method produced the highest accuracy (77%) when used. Our results reveal that employing Harr feature extraction and machine learning techniques, the Random Forest strategy is the most successful way to recognize COVID-19 X-ray pictures. These findings may be pertinent to the development of automated COVID-19 diagnosis tools relying on X-ray images. The recommended model produced results that were competitive when measured against cutting-edge models trained using transfer learning techniques. The suggested model employs 10 times fewer parameters than the most advanced models, which is its key selling point.&nbsp

Controlled delivery of Imatinib mesylate from collagen coated poly(lactic acid) microspheres: In vitro release studies

The development of injectable microspheres for controlled drug delivery to the desired site is a major challenge. We demonstrated the possibility of entrapping an anticancer drug, Imatinib mesylate, in collagen coated biodegradable poly (lactic acid) microspheres with a mean diameter of 10-20 µm. The collagen coating on polymeric matrix surfaces through various surface modification techniques was the current scenario to improve bio-integration of the polymers with the in-vivo system. Here protein adsorption principle is used and various characterization techniques like FTIR, DSC and SEM analysis are used to confirm collagen coating. The reduction in burst release of the Imatinib from the PLA microspheres further confirms its presence and role in controlled release.  This collagen coated PLA microspheres may have potential for the targeted delivery of Imatinib mesylate to treat gastrointestinal stromal tumors, chronic myeloid leukemia cancer

Probing IC/CMB Interpretation for the X-ray knots of AGN through VHE observations

Detection of hard X-ray spectrum from the kilo-parsec scale jet of active galactic nuclei cannot be accounted to the synchrotron emission mechanism from the electron distribution responsible for the radio/optical emission. Alternate explanations are the inverse Compton scattering of cosmic microwave background photons (IC/CMB) or synchrotron emission from a second electron population. When the X-ray emission is interpreted as IC/CMB process, the Compton spectrum peak at GeV energy and were predicted to be the Fermi candidate sources. The non-detection of significant gamma ray flux from these galaxies by Fermi disfavoured the IC/CMB interpretation of the high energy emission. We extend this study to predict the very high energy (VHE) gamma ray emission due to IC/CMB model which can be investigated by Cherenkov Telescope Array(CTA). The model parameters deciding the broadband spectral energy distribution are estimated using analytical approximation of the emissivity functions. The emission model is extrapolated to VHE energy and then compared with the CTAO sensitivity. Particularly, we selected the sources for which the IC/CMB model is not ruled out by initial Fermi observations.Comment: Submitted to MNRAS Main Joura