A new moment-dependent method of probability distributionidentification

A distribution identification method is developed in the paper. The method derives a sequence of characteristic standardized moment ratios for a class of common continuous distribution. The use of the method has been demonstrated via several examples of identifying hypothesized random variables. The method correctly identified the hypothesized distributio

A new moment-dependent method of probability distributionidentification

A distribution identification method is developed in the paper. The method derives a sequence of characteristic standardized moment ratios for a class of common continuous distribution. The use of the method has been demonstrated via several examples of identifying hypothesized random variables. The method correctly identified the hypothesized distributio

Synthesis, Characterization, and Biological Activity of N1-Methyl-2-(1H-1,2,3-Benzotriazol-1-y1)-3-Oxobutan- ethioamide Complexes with Some Divalent Metal (II) Ions

A new series of Zn2+, Cu2+, Ni2+, and Co2+ complexes of N1-methyl-2-(1H-1,2,3-benzotriazol-1-yl)-3-oxobutanethioamide (MBOBT), HL, has been synthesized and characterized by different spectral and magnetic measurements and elemental analysis. IR spectral data indicates that (MBOBT) exists only in the thione form in the solid state while 13C NMR spectrum indicates its existence in thione and thiole tautomeric forms. The IR spectra of all complexes indicate that (MBOBT) acts as a monobasic bidentate ligand coordinating to the metal(II) ions via the keto-oxygen and thiolato-sulphur atoms. The electronic spectral studies showed that (MBOBT) bonded to all metal ions through sulphur and nitrogen atoms based on the positions and intensity of their charge transfer bands. Furthermore, the spectra reflect four coordinate tetrahedral zinc(II), tetragonally distorted copper(II), square planar nickel(II), and cobalt(II) complexes. Thermal decomposition study of the complexes was monitored by TG and DTG analyses under N2 atmosphere. The decomposition course and steps were analyzed and the activation parameters of the nonisothermal decomposition are determined. The isolated metal chelates have been screened for their antimicrobial activities and the findings have been reported and discussed in relation to their structures

Corneal nerve and brain imaging in mild cognitive impairment and dementia

Background: Visual rating of medial temporal lobe atrophy (MTA) is an accepted structural neuroimaging marker of Alzheimer's disease. Corneal confocal microscopy (CCM) is a non-invasive ophthalmic technique that detects neuronal loss in peripheral and central neurodegenerative disorders. Objective: To determine the diagnostic accuracy of CCM for mild cognitive impairment (MCI) and dementia compared to medial temporal lobe atrophy (MTA) rating on MRI. Methods: Subjects aged 60-85 with no cognitive impairment (NCI), MCI, and dementia based on the ICD-10 criteria were recruited. Subjects underwent cognitive screening, CCM, and MTA rating on MRI. Results: 182 subjects with NCI (n = 36), MCI (n = 80), and dementia (n = 66), including AD (n = 19, 28.8%), VaD (n = 13, 19.7%), and mixed AD (n = 34, 51.5%) were studied. CCM showed a progressive reduction in corneal nerve fiber density (CNFD, fibers/mm2) (32.0±7.5 versus 24.5±9.6 and 20.8±9.3, p < 0.0001), branch density (CNBD, branches/mm2) (90.9±46.5 versus 59.3±35.7 and 53.9±38.7, p < 0.0001), and fiber length (CNFL, mm/mm2) (22.9±6.1 versus 17.2±6.5 and 15.8±7.4, p < 0.0001) in subjects with MCI and dementia compared to NCI. The area under the ROC curve (95% CI) for the diagnostic accuracy of CNFD, CNBD, CNFL compared to MTA-right and MTA-left for MCI was 78% (67-90%), 82% (72-92%), 86% (77-95%) versus 53% (36-69%) and 40% (25-55%), respectively, and for dementia it was 85% (76-94%), 84% (75-93%), 85% (76-94%) versus 86% (76-96%) and 82% (72-92%), respectively. Conclusion: The diagnostic accuracy of CCM, a non-invasive ophthalmic biomarker of neurodegeneration, was high and comparable with MTA rating for dementia but was superior to MTA rating for MCI

Molecular Characterization of Leishmania Species Isolated from Cutaneous Leishmaniasis in Yemen

Background: Cutaneous leishmaniasis (CL) is a neglected tropical disease endemic in the tropics and subtropics with a global yearly incidence of 1.5 million. Although CL is the most common form of leishmaniasis, which is responsible for 60% of DALYs lost due to tropical-cluster diseases prevalent in Yemen, available information is very limited. Methodology/Principal Findings: This study was conducted to determine the molecular characterization of Leishmania species isolated from human cutaneous lesions in Yemen. Dermal scrapes were collected and examined for Leishmania amastigotes using the Giemsa staining technique. Amplification of the ribosomal internal transcribed spacer 1(ITS-1) gene was carried out using nested PCR and subsequent sequencing. The sequences from Leishmania isolates were subjected to phylogenetic analysis using the neighbor-joining and maximum parsimony methods. The trees identified Leishmania tropica from 16 isolates which were represented by two sequence types. Conclusions/Significance: The predominance of the anthroponotic species (i.e. L. tropica) indicates the probability of anthroponotic transmission of cutaneous leishmaniasis in Yemen. These findings will help public health authorities to build an effective control strategy taking into consideration person–to-person transmission as the main dynamic of transmissio

Association of Cerebral Ischemia With Corneal Nerve Loss and Brain Atrophy in MCI and Dementia

IntroductionThis study assessed the association of cerebral ischemia with neurodegeneration in mild cognitive impairment (MCI) and dementia.MethodsSubjects with MCI, dementia and controls underwent assessment of cognitive function, severity of brain ischemia, MRI brain volumetry and corneal confocal microscopy.ResultsOf 63 subjects with MCI (n = 44) and dementia (n = 19), 11 had no ischemia, 32 had subcortical ischemia and 20 had both subcortical and cortical ischemia. Brain volume and corneal nerve measures were comparable between subjects with subcortical ischemia and no ischemia. However, subjects with subcortical and cortical ischemia had a lower hippocampal volume (P &lt; 0.01), corneal nerve fiber length (P &lt; 0.05) and larger ventricular volume (P &lt; 0.05) compared to those with subcortical ischemia and lower corneal nerve fiber density (P &lt; 0.05) compared to those without ischemia.DiscussionCerebral ischemia was associated with cognitive impairment, brain atrophy and corneal nerve loss in MCI and dementia

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

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

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries