Neonatal septicemia: isolates and their sensitivity pattern with emergence of Citrobacter septicemia

Background: Septicemia in neonates refers to bacterial infection documented by positive blood culture in the first 28 days of life and is one of the leading causes of neonatal mortality and morbidity in India. The aim of the study was to identify clinical neonatal sepsis cases and isolate responsible microorganism by blood culture and determine sensitivity pattern of isolates in a tertiary care hospital.Methods: It is a hospital based retrospective study involving neonates admitted in department of paediatrics at a medical college hospital. Two hundred twenty five blood samples were collected for blood culture from neonates with clinical sepsis with standard protocol. Isolation of microorganism and antibiotic sensitivity testing was done with disc diffusion method.Results: Blood culture reports were positive in 43.55% cases of clinical sepsis. Among positive blood culture reports gram negative isolates were more frequent (75.51%). Most commonly isolated was Klebsiella species (34.70%), most common gram positive isolate was Streptococcus (10.20%). Prevalence of Citrobacter species isolation was 16.12%. Among gram negative isolates best overall sensitivity was towards levofloxacin (97.3%) followed by amikacin (60.8%). Sensitivity to piperacillin+tazobactam (20.3%) and cefoperazone+sulbactam (23%) were very low. Gram positive isolates had best sensitivity to vancomycin and linezolid.Conclusions: Gram negative organisms (Klebsiella species, Citrobacter species), Streptococcus, Staphylococcus are leading cause of neonatal sepsis. There are high levels of resistance to routinely used antibiotics among them. Therefore results of this study suggest that we should revise our antibiotic treatment policy and emphasize on rationale antibiotic use.

Dental Smart Materials

Dental materials are stable and have greater durability if they do not react with the environment and remain passive. At the same time, it is hoped that the materials will be well accepted and will cause neither harm nor injury. This is an entirely negative approach to material tolerance and biocompatibility. This outlook hides the possibility through which positive gains can be achieved by using materials that behave in a more dynamic fashion in the environment in which they are placed. The current dental materials are improvised. The use of smart materials has made a great revolution in dentistry, which includes the use of restorative materials, such as smart composites, smart ceramics, compomers, resin-modified glass ionomer, amorphous calcium phosphate–releasing pit, and fissure sealants and other materials, such as orthodontic shape memory alloys, smart burs, etc.&nbsp

Esthetic Design

An attractive face is of utmost priority to people. Attractiveness is based on symmetry and various proportions of the face. This helps us to design the smile beautifully and add a lot to facial esthetics. It is a multidisciplinary approach that includes orthodontics, cosmetic dentistry, and plastic surgery. It is veryimportant to carefully analyze and plan the treatment according to the patient’s demand, which will lead us to a beautiful smile

Comprehensive Rare Variant Analysis via Whole-Genome Sequencing to Determine the Molecular Pathology of Inherited Retinal Disease

Inherited retinal disease is a common cause of visual impairment and represents a highly heterogeneous group of conditions. Here, we present findings from a cohort of 722 individuals with inherited retinal disease, who have had whole-genome sequencing (n = 605), whole-exome sequencing (n = 72), or both (n = 45) performed, as part of the NIHR-BioResource Rare Diseases research study. We identified pathogenic variants (single-nucleotide variants, indels, or structural variants) for 404/722 (56%) individuals. Whole-genome sequencing gives unprecedented power to detect three categories of pathogenic variants in particular: structural variants, variants in GC-rich regions, which have significantly improved coverage compared to whole-exome sequencing, and variants in non-coding regulatory regions. In addition to previously reported pathogenic regulatory variants, we have identified a previously unreported pathogenic intronic variant in $\textit{CHM}$ in two males with choroideremia. We have also identified 19 genes not previously known to be associated with inherited retinal disease, which harbor biallelic predicted protein-truncating variants in unsolved cases. Whole-genome sequencing is an increasingly important comprehensive method with which to investigate the genetic causes of inherited retinal disease.This work was supported by The National Institute for Health Research England (NIHR) for the NIHR BioResource – Rare Diseases project (grant number RG65966). The Moorfields Eye Hospital cohort of patients and clinical and imaging data were ascertained and collected with the support of grants from the National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital, National Health Service Foundation Trust, and UCL Institute of Ophthalmology, Moorfields Eye Hospital Special Trustees, Moorfields Eye Charity, the Foundation Fighting Blindness (USA), and Retinitis Pigmentosa Fighting Blindness. M.M. is a recipient of an FFB Career Development Award. E.M. is supported by UCLH/UCL NIHR Biomedical Research Centre. F.L.R. and D.G. are supported by Cambridge NIHR Biomedical Research Centre

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Neonatal septicemia: isolates and their sensitivity pattern with emergence of Citrobacter septicemia

Background: Septicemia in neonates refers to bacterial infection documented by positive blood culture in the first 28 days of life and is one of the leading causes of neonatal mortality and morbidity in India. The aim of the study was to identify clinical neonatal sepsis cases and isolate responsible microorganism by blood culture and determine sensitivity pattern of isolates in a tertiary care hospital.Methods: It is a hospital based retrospective study involving neonates admitted in department of paediatrics at a medical college hospital. Two hundred twenty five blood samples were collected for blood culture from neonates with clinical sepsis with standard protocol. Isolation of microorganism and antibiotic sensitivity testing was done with disc diffusion method.Results: Blood culture reports were positive in 43.55% cases of clinical sepsis. Among positive blood culture reports gram negative isolates were more frequent (75.51%). Most commonly isolated was Klebsiella species (34.70%), most common gram positive isolate was Streptococcus (10.20%). Prevalence of Citrobacter species isolation was 16.12%. Among gram negative isolates best overall sensitivity was towards levofloxacin (97.3%) followed by amikacin (60.8%). Sensitivity to piperacillin+tazobactam (20.3%) and cefoperazone+sulbactam (23%) were very low. Gram positive isolates had best sensitivity to vancomycin and linezolid.Conclusions: Gram negative organisms (Klebsiella species, Citrobacter species), Streptococcus, Staphylococcus are leading cause of neonatal sepsis. There are high levels of resistance to routinely used antibiotics among them. Therefore results of this study suggest that we should revise our antibiotic treatment policy and emphasize on rationale antibiotic use.

Molecular Insights Into Development and Virulence Determinants of Aspergilli: A Proteomic Perspective

Aspergillus species are the major cause of health concern worldwide in immunocompromised individuals. Opportunistic Aspergilli cause invasive to allergic aspergillosis, whereas non-infectious Aspergilli have contributed to understand the biology of eukaryotic organisms and serve as a model organism. Morphotypes of Aspergilli such as conidia or mycelia/hyphae helped them to survive in favorable or unfavorable environmental conditions. These morphotypes contribute to virulence, pathogenicity and invasion into hosts by excreting proteins, enzymes or toxins. Morphological transition of Aspergillus species has been a critical step to infect host or to colonize on food products. Thus, we reviewed proteins from Aspergilli to understand the biological processes, biochemical, and cellular pathways that are involved in transition and morphogenesis. We majorly analyzed proteomic studies on A. fumigatus, A. flavus, A. terreus, and A. niger to gain insight into mechanisms involved in the transition from conidia to mycelia along with the role of secondary metabolites. Proteome analysis of morphotypes of Aspergilli provided information on key biological pathways required to exit conidial dormancy, consortia of virulent factors and mycotoxins during the transition. The application of proteomic approaches has uncovered the biological processes during development as well as intermediates of secondary metabolite biosynthesis pathway. We listed key proteins/ enzymes or toxins at different morphological types of Aspergillus that could be applicable in discovery of novel therapeutic targets or metabolite based diagnostic markers