A micro-architectural evaluation of osteoporotic human femoral heads to guide implant placement in proximal femoral fractures

Peer reviewedPublisher PD

Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome

Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or ‘equivocal’ images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the ‘equivocal’ class using an uncertainty-aware label smoothing loss. We achieved an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.82 (95% CI: 0.80, 0.85), a precision of 0.75 (95% CI: 0.73, 0.78), and a sensitivity of 0.76 (95% CI: 0.73, 0.78) on the internal test set while achieving an (AUROC) of 0.84 (95% CI: 0.81, 0.86), a precision of 0.73 (95% CI: 0.63, 0.69), and a sensitivity of 0.73 (95% CI: 0.70, 0.75) on an external validation set. Further, our results show that this approach improves the model calibration and diagnostic odds ratio of the hypothesized alert tool, making it ideal for clinical decision support systems

Bioenergetic Crisis in ICU-Acquired Weakness Gene Signatures Was Associated With Sepsis-Related Mortality: A Brief Report

OBJECTIVES:. To investigate the relationship between ICU-acquired weakness (ICUAW) signatures and sepsis-related mortality using gene expression from the blood within 24 hours of sepsis onset. DESIGN:. Observational study using differential gene expression analysis. SETTING:. Publicly available gene expression profile GSE54514, single-center medical and surgical ICU. PATIENTS:. Patients with primary bacteremia- and respiratory-triggered sepsis including 8 nonsurvivors and 13 survivors who were 18 years old and older and admitted to ICU. MEASUREMENTS AND MAIN RESULTS:. Among validated 526 ICUAW gene signatures, differential gene expression analysis controlling for age identified 38 significantly expressed genes between nonsurvivors and survivors. Functional enrichment analysis of differentially expressed ICUAW genes identified impaired cadherin binding, sarcomere formation, and energy metabolism among nonsurvivors. CONCLUSIONS:. Our findings demonstrated a biological association between sepsis-related mortality and ICUAW signatures in the early phase of sepsis. Defects in energy metabolism and muscle fiber formation were associated with sepsis-related mortality

Design and synthesis of positional isomers of 5 and 6-bromo-1-(phenyl)sulfonyl]-2-(4-nitrophenoxy)methyl]-1H-benzimida zoles as possible antimicrobial and antitubercular agents

In this Letter, we report the structure activity relationship (SAR) studies on series of positional isomers of 5(6)-bromo-1-(phenyl)sulfonyl]-2-(4-nitrophenoxy)methyl]-1H-benzim idazoles derivatives 7(a-j) and 8(a j) synthesized in good yields and characterized by H-1 NMR, C-13 NMR and mass spectral analyses. The crystal structure of 7a was evidenced by X-ray diffraction study. The newly synthesized compounds were evaluated for their in vitro antibacterial activity against Staphylococcus aureus, (Gram-positive), Escherichia coil and Klebsiella pneumoniae (Gram-negative), antifungal activity against Candida albicans, Aspergillus flavus and Rhizopus sp. and antitubercular activity against Mycobacterium tuberculosis H37Rv, Mycobacterium smegmatis, Mycobacterium fortuitum and MDR-TB strains. The synthesized compounds displayed interesting antimicrobial activity. The compounds 7b, 7e and 7h displayed significant activity against Mycobacterium tuberculosis H37Rv strain

Synthesis of some novel piperidine fused 5-thioxo-1H-1,2,4-triazoles as potential antimicrobial and antitubercular agents

A novel series of analogues based on 5-(1-(4-chloro-3-methoxyphenyl)piperidin-4-yl)-4-phenyl-2H-1,2,4-triazol e-3(4H)-thione core have been synthesized and their potential as antibacterial, antifungal and antitubercular agents was examined. The structure-activity relationship (SAR) studies of these derivatives 5 (a-k) clearly indicate the vital role of lipophilicity as a major factor in enhancing the biological activity of these compounds. Among the compounds screened, 5a, 5c, 5d, 5j and 5k displayed significant activity against Mycobacterium tuberculosis H37Rv strain.Graphic abstrac

Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible.

Established guidelines describe minimum requirements for reporting algorithms in healthcare; it is equally important to objectify the characteristics of ideal algorithms that confer maximum potential benefits to patients, clinicians, and investigators. We propose a framework for ideal algorithms, including 6 desiderata: explainable (convey the relative importance of features in determining outputs), dynamic (capture temporal changes in physiologic signals and clinical events), precise (use high-resolution, multimodal data and aptly complex architecture), autonomous (learn with minimal supervision and execute without human input), fair (evaluate and mitigate implicit bias and social inequity), and reproducible (validated externally and prospectively and shared with academic communities). We present an ideal algorithms checklist and apply it to highly cited algorithms. Strategies and tools such as the predictive, descriptive, relevant (PDR) framework, the Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence (SPIRIT-AI) extension, sparse regression methods, and minimizing concept drift can help healthcare algorithms achieve these objectives, toward ideal algorithms in healthcare

Solution structure of subunit F (Vma7p) of the eukaryotic V1VO ATPase from Saccharomyces cerevesiae derived from SAXS and NMR spectroscopy

Vacuolar ATPases use the energy derived from ATP hydrolysis, catalyzed in the A(3)B(3) sector of the V(1) ATPase to pump protons via the membrane-embedded V(O) sector. The energy coupling between the two sectors occurs via the so-called central stalk, to which subunit F does belong. Here we present the first low resolution structure of recombinant subunit F (Vma7p) of a eukaryotic V-ATPase from Saccharomyces cerevisiae, analyzed by small angle X-ray scattering (SAXS). The protein is divided into a 5.5nm long egg-like shaped region, connected via a 1.5nm linker to a hook-like segment at one end. Circular dichroism spectroscopy revealed that subunit F comprises of 43% α-helix, 32% β-sheet and a 25% random coil arrangement. To determine the localization of the N- and C-termini in the protein, the C-terminal truncated form of F, F(1-94) was produced and analyzed by SAXS. Comparison of the F(1-94) shape with the one of subunit F showed the missing hook-like region in F(1-94), supported by the decreased D(max) value of F(1-94) (7.0nm), and indicating that the hook-like region consists of the C-terminal residues. The NMR solution structure of the C-terminal peptide, F(90-116), was solved, displaying an α-helical region between residues 103 and 113. The F(90-116) solution structure fitted well in the hook-like region of subunit F. Finally, the arrangement of subunit F within the V(1) ATPase is discussed

Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ dysfunction trajectory in critical illnessResearch in context

Summary: Background: Multiple organ dysfunction syndrome (MODS) disproportionately drives morbidity and mortality among critically ill patients. However, we lack a comprehensive understanding of its pathobiology. Identification of genes associated with a persistent MODS trajectory may shed light on underlying biology and allow for accurate prediction of those at-risk. Methods: Secondary analyses of publicly available gene-expression datasets. Supervised machine learning (ML) was used to identify a parsimonious set of genes associated with a persistent MODS trajectory in a training set of pediatric septic shock. We optimized model parameters and tested risk-prediction capabilities in independent validation and test datasets, respectively. We compared model performance relative to an established gene-set predictive of sepsis mortality. Findings: Patients with a persistent MODS trajectory had 568 differentially expressed genes and characterized by a dysregulated innate immune response. Supervised ML identified 111 genes associated with the outcome of interest on repeated cross-validation, with an AUROC of 0.87 (95% CI: 0.85–0.88) in the training set. The optimized model, limited to 20 genes, achieved AUROCs ranging from 0.74 to 0.79 in the validation and test sets to predict those with persistent MODS, regardless of host age and cause of organ dysfunction. Our classifier demonstrated reproducibility in identifying those with persistent MODS in comparison with a published gene-set predictive of sepsis mortality. Interpretation: We demonstrate the utility of supervised ML driven identification of the genes associated with persistent MODS. Pending validation in enriched cohorts with a high burden of organ dysfunction, such an approach may inform targeted delivery of interventions among at-risk patients. Funding: H.R.W.′s NIH R35GM126943 award supported the work detailed in this manuscript. Upon his death, the award was transferred to M.N.A. M.R.A., N.S.P, and R.K were supported by NIH R21GM151703. R.K. was supported by R01GM139967