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Leg length discrepancies are common orthopedic problems with the potential for poor functional outcomes. These are frequently assessed using bilateral leg length radiographs. The objective was to determine whether an artificial intelligence (AI)-based image analysis system can accurately interpret long leg length radiographic images. We built an end-to-end system to analyze leg length radiographs and generate reports like radiologists, which involves measurement of lengths (femur, tibia, entire leg) and angles (mechanical axis and pelvic tilt), describes presence and location of orthopedic hardware, and reports laterality discrepancies. After IRB approval, a dataset of 1,726 extremities (863 images) from consecutive examinations at a tertiary referral center was retrospectively acquired and partitioned into train/validation and test sets. The training set was annotated and used to train a fasterRCNN-ResNet101 object detection convolutional neural network. A second-stage classifier using a EfficientNet-D0 model was trained to recognize the presence or absence of hardware within extracted joint image patches. The system was deployed in a custom web application that generated a preliminary radiology report. Performance of the system was evaluated using a holdout 220 image test set, annotated by 3 musculoskeletal fellowship trained radiologists. At the object detection level, the system demonstrated a recall of 0.98 and precision of 0.96 in detecting anatomic landmarks. Correlation coefficients between radiologist and AI-generated measurements for femur, tibia, and whole-leg lengths were 0.99, with mean error of 1%. Correlation coefficients for mechanical axis angle and pelvic tilt were 0.98 and 0.86, respectively, with mean absolute error of 1°. AI hardware detection demonstrated an accuracy of 99.8%. Automatic quantitative and qualitative analysis of leg length radiographs using deep learning is feasible and holds potential in improving radiologist workflow.

Cryptococcus neoformans var. grubii (Cng) is the most common cause of fungal meningitis, and its prevalence is highest in sub‐Saharan Africa. Patients become infected by inhaling airborne spores or desiccated yeast cells from the environment, where the fungus thrives in avian droppings, trees and soil. To investigate the prevalence and population structure of Cng in southern Africa, we analysed isolates from 77 environmental samples and 64 patients. We detected significant genetic diversity among isolates and strong evidence of geographic structure at the local level. High proportions of isolates with the rare MATa allele were observed in both clinical and environmental isolates; however, the mating‐type alleles were unevenly distributed among different subpopulations. Nearly equal proportions of the MATa and MATα mating types were observed among all clinical isolates and in one environmental subpopulation from the eastern part of Botswana. As previously reported, there was evidence of both clonality and recombination in different geographic areas. These results provide a foundation for subsequent genomewide association studies to identify genes and genotypes linked to pathogenicity in humans.

Multilocus sequence typing (MLST) has become the preferred method for genotyping many biological species, and it is especially useful for analyzing haploid eukaryotes. MLST is rigorous, reproducible, and informative, and MLST genotyping has been shown to identify major phylogenetic clades, molecular groups, or subpopulations of a species, as well as individual strains or clones. MLST molecular types often correlate with important phenotypes. Conventional MLST involves the extraction of genomic DNA and the amplification by PCR of several conserved, unlinked gene sequences from a sample of isolates of the taxon under investigation. In some cases, as few as three loci are sufficient to yield definitive results. The amplicons are sequenced, aligned, and compared by phylogenetic methods to distinguish statistically significant differences among individuals and clades. Although MLST is simpler, faster, and less expensive than whole genome sequencing, it is more costly and time-consuming than less reliable genotyping methods (e.g. amplified fragment length polymorphisms). Here, we describe a new MLST method that uses next-generation sequencing, a multiplexing protocol, and appropriate analytical software to provide accurate, rapid, and economical MLST genotyping of 96 or more isolates in single assay. We demonstrate this methodology by genotyping isolates of the well-characterized, human pathogenic yeast Cryptococcus neoformans.

Endovascular approaches have replaced open surgical revascularization in most patients with mesenteric ischemia; however, flush ostial occlusions may not be amenable to traditional antegrade access. Retrograde mesenteric stenting has been previously described, but this technique requires a formal laparotomy and dissection of the proximal superior mesenteric artery. We present here a modification of this technique that requires only a "mini-laparotomy" and no open vascular repair of the superior mesenteric artery as well as a review of our initial institutional experience with this procedure. Our approach differs from previously described work by minimizing mesenteric dissection, avoiding the need for repair of an arteriotomy, and limiting the size of the laparotomy incision in this population of profoundly comorbid patients.

Cryptococcus neoformans is the leading cause of fungal meningitis worldwide. Previous studies have characterized the cryptococcal transcriptome under various stress conditions, but a comprehensive profile of the C. neoformans transcriptome in the human host has not been attempted. Here, we extracted RNA from yeast cells taken directly from the cerebrospinal fluid (CSF) of two AIDS patients with cryptococcal meningitis prior to antifungal therapy. The patients were infected with strains of C. neoformans var. grubii of molecular type VNI and VNII. Using RNA-seq, we compared the transcriptional profiles of these strains under three environmental conditions (in vivo CSF, ex vivo CSF, and yeast extract-peptone-dextrose [YPD]). Although we identified a number of differentially expressed genes, single nucleotide variants, and novel genes that were unique to each strain, the overall expression patterns of the two strains were similar under the same environmental conditions. Specifically, yeast cells obtained directly from each patient’s CSF were more metabolically active than cells that were incubated ex vivo in CSF. Compared with growth in YPD, some genes were identified as significantly upregulated in both in vivo and ex vivo CSF, and they were associated with genes previously recognized for contributing to pathogenicity. For example, genes with known stress response functions, such as RIM101, ENA1, and CFO1, were regulated similarly in the two clinical strains. Conversely, many genes that were differentially regulated between the two strains appeared to be transporters. These findings establish a platform for further studies of how this yeast survives and produces disease., IMPORTANCE Cryptococcus neoformans, an environmental, opportunistic yeast, is annually responsible for an estimated million cases of meningitis and over 600,000 deaths, mostly among HIV-infected patients in sub-Saharan Africa and Asia. Using RNA-seq, we analyzed the gene expression of two strains of C. neoformans obtained from the cerebrospinal fluid (CSF) of infected patients, thus creating a comprehensive snapshot of the yeasts’ genetic responses within the human body. By comparing the gene expression of each clinical strain under three conditions (in vivo CSF, ex vivo CSF, and laboratory culture), we identified genes and pathways that were uniquely regulated by exposure to CSF and likely crucial for the survival of C. neoformans in the central nervous system. Further analyses revealed genetic diversity between the strains, providing evidence for cryptococcal evolution and strain specificity. This ability to characterize transcription in vivo enables the elucidation of specific genetic responses that promote disease production and progression.

Articular cartilage of young healthy individuals is dynamic and responsive to loading behaviors. The purpose of this study was to evaluate the relationship of cartilage T(1ρ) and T(2) relaxation times with loading kinetics during jumping tasks in healthy young individuals.Fourteen healthy subjects underwent: 1) motion analysis while performing a unilateral hopping task and bilateral drop jumping task; and 2) quantitative imaging using a 3 Tesla MRI for T(1ρ) and T(2) relaxation time analysis. Three dimensional net joint moments and angular impulse was calculated using standard inverse dynamics equations. Average T(1ρ) and T(2) relaxation times and medial-lateral ratios for each were calculated. Multiple regression was used to identify predictors of cartilage relaxation times.Average knee flexion moment during hopping was observed to best predict overall T(1ρ) (R(2)=.185) and T(2) (R(2)=.154) values. Peak knee adduction moment during a drop jump was the best predictor of the T(1ρ) medial-lateral ratio (R(2)=.220). The T(2) medial-lateral ratio was best predicted by average internal rotation moment during the drop jump (R(2)=.174).These data suggest that loads across the knee may affect the biochemistry of the cartilage. In young healthy individuals, higher flexion moments were associated with decreased T(1ρ) and T(2) values, suggesting a potentially beneficial effect. The medial-to-lateral ratio of T(1ρ) and T(2) times appears to be related to the frontal and transverse plane joint mechanics. These data offer promising findings of potentially modifiable parameters associated with cartilage composition.

Magnetic resonance (MR) imaging-based quantitative trabecular bone structure analysis has gained increasing interest in osteoporotic fracture risk assessment and treatment evaluation related to osteoporosis. In vivo MR images of anatomic regions such as the proximal femur and distal tibia are generally acquired with a surface coil in order to obtain sufficient sensitivity and resolution for quantification of the trabeculae. However, these coils introduce intensity inhomogeneities which affect the trabecular bone structure analysis. This work evaluates the applicability of a fully automatic coil correction by nonparametric nonuniform intensity normalization (N3) in the analysis of trabecular bone parameters. The ability to correct for coil-induced intensity inhomogeneity was evaluated ex vivo on proximal femur specimens scanned with both a surface coil and a volume coil, which allowed for a direct evaluation of the performance of the coil correction methods without any major confounding factors. In addition, trabecular bone parameter values were correlated with values from high-resolution peripheral computed tomography (HR-pQCT) scans, and the reproducibility of trabecular bone parameters was evaluated in an in vivo study of repeat hip MR scans. The trabecular bone parameters determined from MR surface coil scans processed with the N3 coil correction method showed significant correlation (p0.05) with corresponding values from homogeneous intensity data in the ex vivo study. This can be compared to the correlation without coil correction (p0.5), and coil correction using low-pass filtering (LPF) (p0.53). The in vivo interscan variability was reduced from 8.9% to 12.8% using LPF-based to 3.6%-8.4% (CV) using N3 coil correction; hence the results showed that N3 is advantageous to LPF-based coil correction. No significant differences in correlation to HR-pQCT data were found for the coil correction methods. The significant correlations with volume coil data and high reproducibility of the N3 processed data imply that N3 coil correction preserve image information while accurately correcting for coil-induced intensity inhomogeneities, which makes it suitable for quantitative analysis of trabecular bone structure from MR images acquired with surface coils.