Diagnostic Test Precision of Modified Carbapenem Inactivation Method and Carbapenemase Nordmann-Poirel Test for Phenotypic Detection of Carbapenemase Production in Enterobacterales: A Systematic Review.

Carbapenem-resistant Enterobacterales, particularly those that produce carbapenemases, pose a significant public health concern due to very limited treatment options. The timely identification of carbapenemase-producing Enterobacterales (CPE) is essential for putting in place efficient infection control measures and selecting appropriate antimicrobial therapies, thereby improving the clinical outcome of the patient. The purpose of this systematic review is to compare the diagnostic accuracy and practicality between two phenotypic tests, namely the modified carbapenem inactivation method (mCIM) and carbapenemase Nordmann-Poirel (Carba NP) test, in detecting carbapenemase production by Enterobacterales and thereby aiding the clinician in making a decision to choose an appropriate test for their phenotypic detection. This systematic review involved combining sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, diagnostic odds ratio with 95% confidence interval (CIs), Forest plot for sensitivity and specificity, and plotting suitable summary receiver operating characteristic curve with the area under the curve. Of the 20 studies included in this review, the overall effect sizes of Carba NP and mCIM with 95% CIs were as follows: sensitivity, 91% (86-96%) and 97% (95-99%); specificity, 93% (88-97%) and 97% (93-100%); PPV, 97% and 98%; NPV, 79% and 90%; accuracy, 93% and 97%; diagnostic odds ratio, 1487.8879 and 8527.5541; and AUC, 0.85 and 1, respectively. In conclusion, the mCIM method showed superior sensitivity (97%), specificity (97%), and accuracy compared to the Carba NP test in detecting carbapenemase production, even though both these methods had a few technical limitations. The Carba NP test is rapid, affordable, and dependable, whereas mCIM is more accurate and cost-effective but time-consuming. We propose that both tests can be reliably used for screening of carbapenemase production in Enterobacterales, as endorsed by the Clinical and Laboratory Standards Institute even in resource-limited clinical laboratories, in the order of prioritizing the mCIM method first and then followed by the Carba NP test when situation demands expedited results.

Line Shape Analysis of 19F NMR-Monitored Chemical Denaturation of a Fold-Switching Protein RfaH Reveals Its Slow Folding Dynamics.

The recent discovery of metamorphic proteins, which can switch between multiple conformations under native conditions, has challenged the well-established one sequence-one structure paradigm of protein folding. This is exemplified in the C-terminal domain of the multidomain transcription factor RfaH, which converts from an α-helical coiled-coil conformation in its autoinhibited state to a ß-barrel conformation upon activation. Here, we use multisite line shape analysis of 19F NMR-monitored equilibrium chemical denaturation measurements of two 19F-labeled variants of full-length RfaH, to show that it folds/unfolds slowly on the NMR time scale, in an apparent all-or-none fashion at physiological pH and room temperature in the 3.3-4.8 M urea concentration range. The significant thermodynamic stability and slow unfolding rate (kinetic stability) are likely responsible for maintaining the closed autoinhibited state of RfaH, preventing spurious interactions with RNA polymerase (RNAP) when not functional. Our results provide a quantitative understanding of the folding-function relationship in the model fold-switching protein RfaH.

Matrix Metalloproteinase-2 (MMP-2): As an Essential Factor in Cancer Progression.

The development of cancer has been a multistep process involving mutation, proliferation, survival, invasion, and metastasis. Of all the characteristics of cancer, metastasis is believed to be the hallmark as it is responsible for the highest number of cancer-related deaths. In connection with this, Matrix metalloproteinases (MMPs), that has a role in metastasis, are one of the novel therapeutic targets. MMPs belong to the family of zinc-dependent endopeptidases and are capable of degrading the components of the extracellular matrix (ECM). The role of MMPs in ECM remodeling includes tissue morphogenesis, uterine cycling, growth, tissue repair, and angiogenesis. During pathological conditions, MMPs play a critical role in the excessive degradation of ECM which includes arthritis, tumour invasion, tumour metastasis, and several other autoimmune disorders. Moreover, they are believed to be involved in many physiological aspects of the cell, such as proliferation, migration, differentiation, angiogenesis, and apoptosis. It is reported that dysregulation of MMP in a variety of cancer subtypes have a dual role in tumour growth and metastasis processes. Further, multiple studies suggest the therapeutic potential of targeting MMP in invading cancer. The expression of MMP-2 correlates with the clinical characteristics of cancer patients, and its expression profile is a new diagnostic and prognostic biomarker for a variety of human diseases. Hence, manipulating the expression or function of MMP-2 may be a potential treatment strategy for different diseases, including cancers. Hence, the present review discusses the therapeutic potential of targeting MMP in various types of cancers and their recent patents.

Therapeutic Plasma exchange therapy in Burns.

Severe burn injury affects the body in many devastating ways, the most severe being systemic inflammatory response syndrome. This results in a myriad of effects like increasing capillary permeability, thereby fluid loss. It also causes a surge in inflammatory mediators like interleukin (IL)-6, which further increases the capillary leak and fluid loss. This results in refractory hypotension in patients despite adequate fluid resuscitation. Plasma exchange has been used in the management of a number of illnesses with a significant inflammatory component, and, therefore can be considered to have a role in burn injury. In our article, we would like to share our experience of using therapeutic plasma exchange therapy in burn patient.

To Study the Correlation of Serum Adenosine Deaminase Levels with Hba1c in Patients of Type 2 Diabetes Mellitus.

Immunological dysfunction is responsible for increased morbidity and mortality due to recurrent infections and hospital admission in Type 2 DM. There are limited studies and markers for the assessment of immunological dysfunction and serum ADA is one of the marker of immunological dysfunction as proven in several studies.The present study is an attempt to correlate ADA as a marker of altered immune function in diabetes mellitus with respect to glycemic control as assessed by HbA1C. MATERIAL: This cross sectional study was conducted in the hospitals attached to Bangalore Medical College and Research Institute. Relavant history taken,Clinical examination and laboratory investigations was done on 90 patients of Type 2 Diabetes mellitus,correlation of HbA1c and serum ADA was done in these individuals taking into consideration the glycemic control. Student t test was used to compare them.Spearman's correlation coefficient was used to see the relationship between the variables and p value of <0.05 was considered significant. OBSERVATION: Among 90 patients in our study 57 were males and 33 were females. Mean ± SD of HbA1C was 9.85± 2.73.Mean ± SD of serum ADA levels was 40.39 ± 4.69. Spearman's correlation coefficient in our study was 0.728 indicating a strong correlation between serum ADA and HbA1C. There was a linear correlation between HbA1C levels and serum ADA and the correlation was statistically significant P Value <0.001. CONCLUSION: Metabolic and immunological disturbance are two important key factors in type 2 diabetes mellitus which is a leading cause of morbidity and mortality.ADA is an enzyme, which is considered as a good marker for cell mediated immunity.Our study showed elevated serum ADA activity in poorly controlled diabetic individuals and a strong correlation of HbA1C with serum ADA levels which was statistically significant indicating poor the glycemic control more the immunological dysfunction.

Augmenting Transfer Learning with Feature Extraction Techniques for Limited Breast Imaging Datasets.

Computer aided detection (CADe) and computer aided diagnostic (CADx) systems are ongoing research areas for identifying lesions among complex inner structures with different pixel intensities, and for medical image classification. There are several techniques available for breast cancer detection and diagnosis using CADe and CADx systems. However, some of these systems are not accurate enough or suffer from lack of sufficient data. For example, mammography is the most commonly used breast cancer detection technique, and there are several CADe and CADx systems based on mammography, because of the huge dataset that is publicly available. But, the number of cancers escaping detection with mammography is substantial, particularly in dense-breasted women. On the other hand, digital breast tomosynthesis (DBT) is a new imaging technique, which alleviates the limitations of the mammography technique. However, the collections of huge amounts of the DBT images are difficult as it is not publicly available. In such cases, the concept of transfer learning can be employed. The knowledge learned from a trained source domain task, whose dataset is readily available, is transferred to improve the learning in the target domain task, whose dataset may be scarce. In this paper, a two-level framework is developed for the classification of the DBT datasets. A basic multilevel transfer learning (MLTL) based framework is proposed to use the knowledge learned from general non-medical image datasets and the mammography dataset, to train and classify the target DBT dataset. A feature extraction based transfer learning (FETL) framework is proposed to further improve the classification performance of the MLTL based framework. The FETL framework looks at three different feature extraction techniques to augment the MLTL based framework performance. The area under receiver operating characteristic (ROC) curve of value 0.89 is obtained, with just 2.08% of the source domain (non-medical) dataset, 5.09% of the intermediate domain (mammography) dataset, and 3.94% of the target domain (DBT) dataset, when compared to the dataset reported in literature.