Parameterized Complexity of Perfectly Matched Sets

For an undirected graph G, a pair of vertex disjoint subsets (A, B) is a pair of perfectly matched sets if each vertex in A (resp. B) has exactly one neighbor in B (resp. A). In the above, the size of the pair is |A| (= |B|). Given a graph G and a positive integer k, the Perfectly Matched Sets problem asks whether there exists a pair of perfectly matched sets of size at least k in G. This problem is known to be NP-hard on planar graphs and W[1]-hard on general graphs, when parameterized by k. However, little is known about the parameterized complexity of the problem in restricted graph classes. In this work, we study the problem parameterized by k, and design FPT algorithms for: i) apex-minor-free graphs running in time 2^O(?k)? n^O(1), and ii) K_{b,b}-free graphs. We obtain a linear kernel for planar graphs and k^?(d)-sized kernel for d-degenerate graphs. It is known that the problem is W[1]-hard on chordal graphs, in fact on split graphs, parameterized by k. We complement this hardness result by designing a polynomial-time algorithm for interval graphs

Cupriavidus gilardii Pneumonia in a Young Patient with Chronic Kidney Disease: A Case Report

Cupriavidus gilardii (C. gilardii) is a Gram-negative, motile, non sporulating, non lactose fermenting bacterium. It was first identified by Coenye et al., and has a complex taxonomy, often being misidentified as Wausteria gilardii, Ralstonia gilardii. It is commonly found in ecosystems containing plants and heavy metal-contaminated soil and is rarely isolated from clinical samples with no clear evidence of its clinical significance. The pathogenic nature of C. gilardii in respiratory ailments, particularly in patients with cystic fibrosis, is still unclear. This case report presents a 19-year-old female with Chronic Kidney Disease (CKD) who developed pneumonia caused by C. gilardii. The report also includes the sensitivity pattern of the bacterium to guide physicians in treating these rare pathogens

Tardy Aschner-Dagnini Reflex following Topical Pterygium Surgery: A Rare Case Report

The Aschner-Dagnini reflex, also known as the Oculocardiac Reflex (OCR) or Trigeminovagal Reflex (TVR), is characterised by a reduction in heart rate due to direct pressure on the globe or traction on the Extraocular Muscles (EOM). It was first described in 1908 and is observed during strabismus surgery. However, it has also been reported following other ophthalmic procedures such as pterygium surgery or gonioscopy, as well as after facial trauma or regional anaesthesia. Sinus bradycardia is the most common presentation, accompanied by nausea and dizziness in conscious patients. In severe cases, it may also result in reduced blood pressure and life-threatening emergencies, including cardiac arrhythmias and arrest. Hereby, the authors present a rare case report of a 34-year-old male with delayed onset OCR following pterygium surgery under topical anaesthesia. The case was managed conservatively, as described in the case report, and the patient had a good recovery. To the best of authors’ knowledge, the present is the first reported case of OCR in the early postoperative period, following pterygium surgery

Aerococcus viridans Bacteraemia in a COVID-19 Positive Patient: A Rare Case Report from Northern India

Aerococcus viridans is a rare Gram positive microorganism identified largely as environmental or skin contaminants. With the advent of an increase in the immunosuppressed population due to diabetes, the use of steroids and the Coronavirus Disease 2019 (COVID-19) pandemic, this bacteria caused a variety of infections like bacteraemia, urinary tract infections, and endocarditis. The use of Matrix-Assisted Laser Desorption Ionisation Time-of-Flight Mass-Spectrometry (MALDI-TOF MS), a unique technique of microorganism identification, has placed Aerococci among human pathogens, capable of causing infection among immunocompromised patients. The present case was of a 48-year-old female presented with dry cough, high-grade fever associated with chills and rigors, and generalised body ache and weakness for the past one week. She was a known case of bronchial asthma. She tested positive for COVID-19 and over the course of hospital stay, her BACTEC blood culture performed due to high fever which flagged positive indicated her as a case of Aerococcus viridans bacteraemia. Despite of all the efforts she developed respiratory distress followed by an episode of asystole following which she could not be revive

Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains

Multimodal medical image fusion is effectuated to minimize the redundancy while augmenting the necessary information from the input images acquired using different medical imaging sensors. The sole aim is to yield a single fused image, which could be more informative for an efficient clinical analysis. This paper presents a two-stage multimodal fusion framework using the cascaded combination of stationary wavelet transform (SWT) and non sub-sampled Contourlet transform (NSCT) domains for images acquired using two distinct medical imaging sensor modalities (i.e., magnetic resonance imaging and computed tomography scan). The major advantage of using a cascaded combination of SWT and NSCT is to improve upon the shift variance, directionality, and phase information in the finally fused image. The first stage employs a principal component analysis algorithm in SWT domain to minimize the redundancy. Maximum fusion rule is then applied in NSCT domain at second stage to enhance the contrast of the diagnostic features. A quantitative analysis of fused images is carried out using dedicated fusion metrics. The fusion responses of the proposed approach are also compared with other state-of-the-art fusion approaches; depicting the superiority of the obtained fusion results

A Rare Case of Aerococcus viridans Meningitis in a Patient with Trigeminal Nerve Schwannoma

The genus Aerococcus spp. comprise microaerophilic, catalase-negative, Gram-positive cocci that show alpha-haemolytic growth on blood agar. They have a tendency to divide on two planes at a 90° angle, and rapid multiplication leads to the formation of Grampositive cocci in tetrads and irregular clusters. Aerococcus spp. are capable of causing invasive and fatal systemic illnesses, such as endocarditis, bactereamia, arthritis, and meningitis. Due to evolving diagnostic tools, it is now identified as a pathogen in a variety of disorders instead of being considered a contaminant. Most isolates are susceptible to penicillins, but there is increasing resistance to cephalosporins, ciprofloxacin, cotrimoxazole, clindamycin, vancomycin, and tetracycline. Here, authors present a rare case of Aerococcus viridans meningitis in a patient who underwent surgical excision of a left trigeminal Schwannoma, along with the drug susceptibility pattern resistant to most first-line antibiotics used against isolates from Streptococci spp., except doxycycline

In-vitro Activity of Isepamicin against Gram-negative Bacteria in Comparison to Other Aminoglycosides Routinely used at a Teaching Hospital in Northern India

Background Isepamicin is a 1-N-S-a-hydroxy-b-aminopropionyl derivative of gentamicin B and the spectrum of pathogenic microorganisms covered by it and its effectiveness is similar to that of amikacin except the action of aminoglycoside inhibitor enzymes is ineffectual on it

Table_1_Outbreak of colistin resistant, carbapenemase (blaNDM, blaOXA-232) producing Klebsiella pneumoniae causing blood stream infection among neonates at a tertiary care hospital in India.docx

Infections caused by multi-drug resistant Klebsiella pneumoniae are a leading cause of mortality and morbidity among hospitalized patients. In neonatal intensive care units (NICU), blood stream infections by K. pneumoniae are one of the most common nosocomial infections leading to poor clinical outcomes and prolonged hospital stays. Here, we describe an outbreak of multi-drug resistant K. pneumoniae among neonates admitted at the NICU of a large tertiary care hospital in India. The outbreak involved 5 out of 7 neonates admitted in the NICU. The antibiotic sensitivity profiles revealed that all K. pneumoniae isolates were multi-drug resistant including carbapenems and colistin. The isolates belonged to three different sequence types namely, ST-11, ST-16 and ST-101. The isolates harboured carbapenemase genes, mainly blaNDM-1, blaNDM-5 and blaOXA-232 besides extended-spectrum β-lactamases however the colistin resistance gene mcr-1, mcr-2 and mcr-3 could not be detected. Extensive environmental screening of the ward and healthcare personnel led to the isolation of K. pneumoniae ST101 from filtered incubator water, harboring blaNDM-5, blaOXA-232 and ESBL genes (blaCTX-M) but was negative for the mcr genes. Strict infection control measures were applied and the outbreak was contained. This study emphasizes that early detection of such high-risk clones of multi-drug resistant isolates, surveillance and proper infection control practices are crucial to prevent outbreaks and further spread into the community.</p

DataSheet_1_Predicting IDH subtype of grade 4 astrocytoma and glioblastoma from tumor radiomic patterns extracted from multiparametric magnetic resonance images using a machine learning approach.pdf

Background and purposeSemantic imaging features have been used for molecular subclassification of high-grade gliomas. Radiomics-based prediction of molecular subgroups has the potential to strategize and individualize therapy. Using MRI texture features, we propose to distinguish between IDH wild type and IDH mutant type high grade gliomas.MethodsBetween 2013 and 2020, 100 patients were retrospectively analyzed for the radiomics study. Immunohistochemistry of the pathological specimen was used to initially identify patients for the IDH mutant/wild phenotype and was then confirmed by Sanger’s sequencing. Image texture analysis was performed on contrast-enhanced T1 (T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on MR image slices followed by single-slice multiple sampling image augmentation. Both whole tumor multislice segmentation and single-slice multiple sampling approaches were used to arrive at the best model. Radiomic features were extracted, which included first-order features, second-order (GLCM—Grey level co-occurrence matrix), and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression, followed by radiomic classification using Support Vector Machine (SVM) and a 10-fold cross-validation strategy for model development. The area under the Receiver Operator Characteristic (ROC) curve and predictive accuracy were used as diagnostic metrics to evaluate the model to classify IDH mutant and wild-type subgroups.ResultsMultislice analysis resulted in a better model compared to the single-slice multiple-sampling approach. A total of 164 MR-based texture features were extracted, out of which LASSO regression identified 14 distinctive GLCM features for the endpoint, which were used for further model development. The best model was achieved by using combined T1C and T2W MR images using a Quadratic Support Vector Machine Classifier and a 10-fold internal cross-validation approach, which demonstrated a predictive accuracy of 89% with an AUC of 0.89 for each IDH mutant and IDH wild subgroup.ConclusionA machine learning classifier of radiomic features extracted from multiparametric MRI images (T1C and T2w) provides important diagnostic information for the non-invasive prediction of the IDH mutant or wild-type phenotype of high-grade gliomas and may have potential use in either escalating or de-escalating adjuvant therapy for gliomas or for using targeted agents in the future.</p

DataSheet_2_Predicting IDH subtype of grade 4 astrocytoma and glioblastoma from tumor radiomic patterns extracted from multiparametric magnetic resonance images using a machine learning approach.pdf

Background and purposeSemantic imaging features have been used for molecular subclassification of high-grade gliomas. Radiomics-based prediction of molecular subgroups has the potential to strategize and individualize therapy. Using MRI texture features, we propose to distinguish between IDH wild type and IDH mutant type high grade gliomas.MethodsBetween 2013 and 2020, 100 patients were retrospectively analyzed for the radiomics study. Immunohistochemistry of the pathological specimen was used to initially identify patients for the IDH mutant/wild phenotype and was then confirmed by Sanger’s sequencing. Image texture analysis was performed on contrast-enhanced T1 (T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on MR image slices followed by single-slice multiple sampling image augmentation. Both whole tumor multislice segmentation and single-slice multiple sampling approaches were used to arrive at the best model. Radiomic features were extracted, which included first-order features, second-order (GLCM—Grey level co-occurrence matrix), and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression, followed by radiomic classification using Support Vector Machine (SVM) and a 10-fold cross-validation strategy for model development. The area under the Receiver Operator Characteristic (ROC) curve and predictive accuracy were used as diagnostic metrics to evaluate the model to classify IDH mutant and wild-type subgroups.ResultsMultislice analysis resulted in a better model compared to the single-slice multiple-sampling approach. A total of 164 MR-based texture features were extracted, out of which LASSO regression identified 14 distinctive GLCM features for the endpoint, which were used for further model development. The best model was achieved by using combined T1C and T2W MR images using a Quadratic Support Vector Machine Classifier and a 10-fold internal cross-validation approach, which demonstrated a predictive accuracy of 89% with an AUC of 0.89 for each IDH mutant and IDH wild subgroup.ConclusionA machine learning classifier of radiomic features extracted from multiparametric MRI images (T1C and T2w) provides important diagnostic information for the non-invasive prediction of the IDH mutant or wild-type phenotype of high-grade gliomas and may have potential use in either escalating or de-escalating adjuvant therapy for gliomas or for using targeted agents in the future.</p