Channel-Boosted and Transfer Learning Convolutional Neural Network-Based Osteoporosis Detection from CT Scan, Dual X-Ray, and X-Ray Images.

Osteoporosis is a word used to describe a condition in which bone density has been diminished as a result of inadequate bone tissue development to counteract the elimination of old bone tissue. Osteoporosis diagnosis is made possible by the use of medical imaging technologies such as CT scans, dual X-ray, and X-ray images. In practice, there are various osteoporosis diagnostic methods that may be performed with a single imaging modality to aid in the diagnosis of the disease. The proposed study is to develop a framework, that is, to aid in the diagnosis of osteoporosis which agrees to all of these CT scans, X-ray, and dual X-ray imaging modalities. The framework will be implemented in the near future. The proposed work, CBTCNNOD, is the integration of 3 functional modules. The functional modules are a bilinear filter, grey-level zone length matrix, and CB-CNN. It is constructed in a manner that can provide crisp osteoporosis diagnostic reports based on the images that are fed into the system. All 3 modules work together to improve the performance of the proposed approach, CBTCNNOD, in terms of accuracy by 10.38%, 10.16%, 7.86%, and 14.32%; precision by 11.09%, 9.08%, 10.01%, and 16.51%; sensitivity by 9.77%, 10.74%, 6.20%, and 12.78%; and specificity by 11.01%, 9.52%, 9.5%, and 15.84%, while requiring less processing time of 33.52%, 17.79%, 23.34%, and 10.86%, when compared to the existing techniques of RCETA, BMCOFA, BACBCT, and XSFCV, respectively.

Mutation analysis of WNT4 gene in SRY negative 46,XX DSD patients with Mullerian agenesis and/or gonadal dysgenesis- An Indian study.

Developmental disruption of the Mullerian duct and gonads in females leads to Mullerian agenesis and gonadal dysgenesis, respectively. These two structural abnormalities are coming under the 46,XX DSD (Disorders of Sexual Development) classification, the majority of cases the aetiology remains elusive. Without the SRY gene, WNT4 plays a key role in female reproductive structure development. Since there are no studies that explored the involvement of the WNT4 gene in Indian 46,XX DSD patients, we analysed the role of WNT4 in Indian 46,XX DSD patients with Mullerian agenesis and/or Gonadal dysgenesis. In our study, we recruited 103 adolescent girls with primary amenorrhea. After the cytogenetic and SRY gene analysis, we included thirty-two 46,XX DSD patients with Mullerian agenesis and/or gonadal dysgenesis for WNT4 gene mutation analysis. PCR sequencing was performed for all the coding exons of the WNT4 gene. Bioinformatic tools like Mutation Taster, Human Splicing Finder, and miRDB were used. We observed single nucleotide variations in three patients. One patient showed a known synonymous polymorphism (c.861C > T; p.G287G, rs544988174). miRDB data revealed the absence of microRNA regulatory sites in this region. The other two cases carried a nucleotide substitution in intronic regions and did not affect the normal splicing mechanism. In conclusion, we could not find any indication about WNT4 involvement in the disease condition. In the future, WNT4 promoter analysis in these patients and molecular characterization of the WNT4 coding and promoter region in more patients are needed to link WNT4 variants with these structural abnormalities.

3D Convolutional Neural Network Framework with Deep Learning for Nuclear Medicine.

Though artificial intelligence (AI) has been used in nuclear medicine for more than 50 years, more progress has been made in deep learning (DL) and machine learning (ML), which have driven the development of new AI abilities in the field. ANNs are used in both deep learning and machine learning in nuclear medicine. Alternatively, if 3D convolutional neural network (CNN) is used, the inputs may be the actual images that are being analyzed, rather than a set of inputs. In nuclear medicine, artificial intelligence reimagines and reengineers the field's therapeutic and scientific capabilities. Understanding the concepts of 3D CNN and U-Net in the context of nuclear medicine provides for a deeper engagement with clinical and research applications, as well as the ability to troubleshoot problems when they emerge. Business analytics, risk assessment, quality assurance, and basic classifications are all examples of simple ML applications. General nuclear medicine, SPECT, PET, MRI, and CT may benefit from more advanced DL applications for classification, detection, localization, segmentation, quantification, and radiomic feature extraction utilizing 3D CNNs. An ANN may be used to analyze a small dataset at the same time as traditional statistical methods, as well as bigger datasets. Nuclear medicine's clinical and research practices have been largely unaffected by the introduction of artificial intelligence (AI). Clinical and research landscapes have been fundamentally altered by the advent of 3D CNN and U-Net applications. Nuclear medicine professionals must now have at least an elementary understanding of AI principles such as neural networks (ANNs) and convolutional neural networks (CNNs).

A Regional Pooling Intervention in a High-Throughput COVID-19 Diagnostic Laboratory to Enhance Throughput, Save Resources and Time Over a Period of 6 Months.

An effective and rapid diagnosis has great importance in tackling the ongoing COVID-19 pandemic through isolation of the infected individuals to curb the transmission and initiation of specialized treatment for the disease. It has been proven that enhanced testing capacities contribute to efficiently curbing SARS-CoV-2 transmission during the initial phases of the outbreaks. RT-qPCR is considered a gold standard for the diagnosis of COVID-19. However, in resource-limited countries expenses for molecular diagnosis limits the diagnostic capacities. Here, we present interventions of two pooling strategies as 5 sample pooling (P-5) and 10 sample pooling (P-10) in a high-throughput COVID-19 diagnostic laboratory to enhance throughput and save resources and time over a period of 6 months. The diagnostic capacity was scaled-up 2.15-folds in P-5 and 1.8-fold in P-10, reagents (toward RNA extraction and RT-qPCR) were preserved at 75.24% in P-5 and 86.21% in P-10, and time saved was 6,290.93 h in P-5 and 3147.3 h in P-10.

Bacterial profile of bovine mastitis in Ethiopia: a systematic review and meta-analysis.

Background: Bovine mastitis is the commonest episode of infection in the dairy industry, which often occurs after damage of epithelial cells lining the teat duct. It is ranked as the second most important cause of milk production loss directly and a devastating disease with a higher incidence leading to the culling of dairy cows. Thus, this systematic review and meta-analysis is aimed to quantitatively estimate the current status of mastitis in general and bacterial mastitis particular in Ethiopia. Methods: A literature search was carried from major databases and indexing services including PubMed, Google Scholar, Science Direct. Also, local institution repositories were searched to retrieve unpublished MSc and PhD theses. All studies were included addressing the prevalence of mastitis and bacterial isolates conducted in Ethiopia. Microsoft Excel was used to extract data and was imported to R Studio for the analyses. The random-effects model at a 95% confidence level was used for pooled estimates of outcomes. The degree of heterogeneity was computed by Higgins's I2 statistics. Publication bias was checked by using the funnel plots of standard error augmented by Begg's and Egger's tests. Results: A total of 46 studies with 15,780 cows were included in this study. All studies have collected 18,478 suspected samples for bacterial isolation. While pooled prevalence estimate of mastitis was 47.6%, the bacterial isolates pooled prevalence was 33.1%. The bacterial mastitis was 6.5% in cows infected by clinical patients and was 28.3% subclinical patients. The common isolates were Staph aureus, Strep species, Staph epidermis, Escherchia coli, Corynebacterium bovis, Coagulase Negative Staphylococcus and Klebsiella species. A univariate meta-regression analysis evidenced that the type of mastitis and management system was a possible source of heterogeneity (P-value = 0.001). Conclusion: The pooled prevalence of bacterial mastitis in Ethiopian dairy cattle was high. The analysis showed bacterial pathogens like Staphylococcus aureus, Staph epidermis, Strep species and Coagulase Negative Staphylococcus are majorly accounted for bovine mastitis in Ethiopia. Therefore, the highly prevalent and commonly isolated pathogens cause contagious mastitis which require immediate attention by dairy producers to put under control by devising robust mastitis prevention and control interventions.

Guidelines for the management of common congenital heart diseases in India: A consensus statement on indications and timing of intervention.

INTRODUCTION: A number of guidelines are available for management of congenital heart diseases from infancy to adult life. However, these guidelines are for patients living in high-income countries. Separate guidelines, applicable to Indian children, are required when recommending an intervention for congenital heart diseases, as often these patients present late in the course of the disease and may have co-existing morbidities and malnutrition. PROCESS: Guidelines emerged following expert deliberations at the National Consensus Meeting on Management of Congenital Heart Diseases in India, held on the 10th and 11th of August, 2018 at the All India Institute of Medical Sciences. OBJECTIVES: The aim of the study was to frame evidence-based guidelines for (i) indications and optimal timing of intervention in common congenital heart diseases and (ii) follow-up protocols for patients who have undergone cardiac surgery/catheter interventions for congenital heart diseases. RECOMMENDATIONS: Evidence-based recommendations are provided for indications and timing of intervention in common congenital heart diseases, including left-to-right shunts, obstructive lesions, and cyanotic congenital heart diseases. In addition, protocols for follow-up of postsurgical patients are also described.

Stability of nano-sized permethrin in its colloidal state and its effect on the physiological and biochemical profile of Culex tritaeniorhynchus larvae.

The occurrence of pesticidal pollution in the environment and the resistance in the mosquito species makes an urge for the safer and an effective pesticide. Permethrin, a poorly water-soluble pyrethroid pesticide, was formulated into a hydrodispersible nanopowder through rapid solvent evaporation of pesticide-loaded oil in water microemulsion. Stability studies confirmed that the nanopermethrin dispersion was stable in paddy field water for 5 days with the mean particle sizes of 175.3 ± 0.75 nm and zeta potential of -30.6 ± 0.62 mV. The instability rate of the nanopermethrin particles was greater in alkaline (pH 10) medium when compared with the neutral (pH 7) and acidic (pH 4) dispersion medium. The colloidal dispersion at 45°C was found to be less stable compared with the dispersions at 25 and 5°C. The 12- and 24-h lethal indices (LC50) for nanopermethrin were found to be 0.057 and 0.014 mg l-1, respectively. These results were corroborative with the severity of damages observed in the mosquito larvae manifested in epithelial cells and the evacuation of the midgut contents. Further, the results were substantiated by the decrease in cellular biomolecules and biomarker enzyme activity in nanopermethrin treated larvae when compared to bulk and control treatment.

Therapeutic effects of gold nanoparticles synthesized using Musa paradisiaca peel extract against multiple antibiotic resistant Enterococcus faecalis biofilms and human lung cancer cells (A549).

Botanical-mediated synthesis of nanomaterials is currently emerging as a cheap and eco-friendly nanotechnology, since it does not involve the use of toxic chemicals. In the present study, we focused on the synthesis of gold nanoparticles using the aqueous peel extract of Musa paradisiaca (MPPE-AuNPs) following a facile and cheap fabrication process. The green synthesized MPPE-AuNPs were bio-physically characterized by UV-Vis spectroscopy, FTIR, XRD, TEM, Zeta potential analysis and EDX. MPPE-AuNPs were crystalline in nature, spherical to triangular in shape, with particle size ranging within 50 nm. The biofilm inhibition activity of MPPE-AuNPs was higher against multiple antibiotic resistant (MARS) Gram-positive Enterococcus faecalis. Light and confocal laser scanning microscopic observations evidenced that the MPPE-AuNPs effectively inhibited the biofilm of E. faecalis when tested at 100 µg mL-1. Cytotoxicity studies demonstrated that MPPE-AuNPs were effective in inhibiting the viability of human A549 lung cancer cells at higher concentrations of 100 µg mL-1. The morphological changes in the MPPE-AuNPs treated A549 lung cancer cells were visualized under phase-contrast microscopy. Furthermore, the ecotoxicity of MPPE-AuNPs on the freshwater micro crustacean Ceriodaphnia cornuta were evaluated. Notably, no mortality was recorded in MPPE-AuNPs treated C. cornuta at 250 µg mL-1. This study concludes that MPPE-AuNPs are non-toxic, eco-friendly and act as a multipurpose potential biomaterial for biomedical applications.