Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.

Epithelioid Sarcoma of the Spine: A Review of Literature and Case Report

Epithelioid sarcoma is a rare malignant mesenchymal tumor that represents less than 1% of soft-tissue sarcomas. Despite its slow growth, the overall prognosis is poor with a high rate of local recurrence, lymph-node spread, and hematogenous metastasis. Primary epithelioid sarcoma arising from the spine is extremely rare, with limited data in the literature. We review the existing literature regarding spinal epithelioid sarcoma and report a case of epithelioid sarcoma arising from the spinal cord. A 54 year old male presented with a 1-month history of progressive left upper-limb weakness and numbness. Magnetic resonance imaging (MRI) of the spine showed an enhancing intramedullary mass at the level of T1 also involving the left T1 nerve root. Systemic radiological examination revealed no other lesion at presentation. Surgical excision of the mass was performed, and histology was consistent with epithelioid sarcoma of the spine. Despite adjuvant radiotherapy, there was aggressive local recurrence and development of intracranial metastatic spread. The patient died of the disease within 5 months from presentation. To the best of our knowledge, spinal epithelioid sarcoma arising from the spinal cord has not yet been reported. We review the challenges in diagnosis, surgical treatment, and oncologic outcome of this case.

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

Isospin Mixing Reveals ^{30}P(p,γ)^{31}S Resonance Influencing Nova Nucleosynthesis

The thermonuclear ^{30}P(p,γ)^{31}S reaction rate is critical for modeling the final elemental and isotopic abundances of ONe nova nucleosynthesis, which affect the calibration of proposed nova thermometers and the identification of presolar nova grains, respectively. Unfortunately, the rate of this reaction is essentially unconstrained experimentally, because the strengths of key ^{31}S proton capture resonance states are not known, largely due to uncertainties in their spins and parities. Using the β decay of ^{31}Cl, we have observed the β-delayed γ decay of a ^{31}S state at E_{x}=6390.2(7)  keV, with a ^{30}P(p,γ)^{31}S resonance energy of E_{r}=259.3(8)  keV, in the middle of the ^{30}P(p,γ)^{31}S Gamow window for peak nova temperatures. This state exhibits isospin mixing with the nearby isobaric analog state at E_{x}=6279.0(6)  keV, giving it an unambiguous spin and parity of 3/2^{+} and making it an important l=0 resonance for proton capture on ^{30}P.

Prognostic significance of lymph node density in squamous cell carcinoma of the tongue

BACKGROUND

The prognostic significance of lymph node density in oral squamous cell carcinoma (OSCC) has been well recognized. However, its use in a specific subsite of the tongue has not been evaluated. The purpose of this study was to determine the prognostic significance of lymph node density in tongue squamous cell carcinoma (SCC).

METHODS

A retrospective analysis of 99 patients with tongue SCC who underwent primary curative resection and neck dissection was conducted. Overall survival (OS) and disease-specific survival (DSS) was used to evaluate the prognostic significance of lymph node density.

RESULTS

Lymph node density (using a cutoff of 0.06) was shown to be an independent predictor of OS and DSS. The impact of lymph node density on OS and DSS remained significant on multivariate analysis, whereas conventional nodal staging was not. An alternative staging strategy incorporating depth of invasion and lymph node density performs better than conventional TNM staging in predicting survival.

CONCLUSION

Our data suggest that lymph node density is a reliable and applicable predictor of prognosis in patients with tongue SCC. © 2015 Wiley Periodicals, Inc. Head Neck 38: E859-E866, 2016.

Spin-imbalanced quasi-two-dimensional Fermi gases

We measure the density profiles for a Fermi gas of (6)Li containing N(1) spin-up atoms and N(2) spin-down atoms, confined in a quasi-two-dimensional geometry. The spatial profiles are measured as a function of spin imbalance N(2)/N(1) and interaction strength, which is controlled by means of a collisional (Feshbach) resonance. The measured cloud radii and central densities are in disagreement with mean-field Bardeen-Cooper-Schrieffer theory for a true two-dimensional system. We find that the data for normal-fluid mixtures are reasonably well fit by a simple two-dimensional polaron model of the free energy. Not predicted by the model is a phase transition to a spin-balanced central core, which is observed above a critical value of N(2)/N(1). Our observations provide important benchmarks for predictions of the phase structure of quasi-two-dimensional Fermi gases.

Population structure, mitochondrial polyphyly and the repeated loss of human biting ability in anopheline mosquitoes from the southwest Pacific

Australia and New Guinea contain high levels of endemism and biodiversity, yet there have been few evaluations of population-level genetic diversity in fauna occurring throughout the Australo-Papuan region. Using extensive geographical sampling, we examined and compared the phylogenetic relationships, phylogeography and population structure of Anopheles farauti, An. hinesorum and An. irenicus throughout their ranges in the southwest Pacific using mitochondrial (mtDNA COI) and nuclear (ribosomal protein S9 and ribosomal DNA ITS2) loci. Phylogenetic analyses suggest that the ability to utilize humans as hosts has been lost repeatedly, coincident with independent colonizations of the Solomon Islands. As some of the species under investigation transmit malaria in the region, this is a medically important finding. Maximum likelihood and Bayesian phylogenetic analyses of nuclear loci also showed that the three species are monophyletic. However, putative introgression of An. hinesorum mtDNA onto a nuclear background of An. farauti was evident in populations from Queensland, Torres Strait and southern New Guinea. Haplotype networks and pairwise F(ST) values show that there is significant genetic structure within New Guinea and Australia in both An. farauti and An. hinesorum, consistent with a long-term history of low gene flow among populations.

Polaron-to-polaron transitions in the radio-frequency spectrum of a quasi-two-dimensional Fermi gas

We measure radio-frequency spectra for a two-component mixture of a 6Li atomic Fermi gas in a quasi-two-dimensional regime with the Fermi energy comparable to the energy level spacing in the tightly confining potential. Near the Feshbach resonance, we find that the observed resonances do not correspond to transitions between confinement-induced dimers. The spectral shifts can be fit by assuming transitions between noninteracting polaron states in two dimensions.

Phase transfer of hydrophilic, cyclodextrin-modified gold nanoparticles to chloroform solutions

The preparation and characterization of gold nanoparticles (approximately 3 nm in diameter) capped with thiolated alpha- and beta-cyclodextrins (alpha and beta-CD) is described. The CD-capped nanoparticles are hydrophilic and bind ferrocene derivatives as evidenced by electrochemical and (1)H NMR spectroscopic measurements. The binding interactions of the CD-capped nanoparticles with a series of five alkyldimethyl(ferrocenylmethyl)ammonium ions (the alkyl group is propyl for compound 1, heptyl for 2, dodecyl for 3, hexadecyl for 4, and docosyl for 5) can be utilized for the phase transfer of the hydrophilic, CD-capped nanoparticles into a nonpolar chloroform phase. Only 3, 4, and 5 act as effective phase transfer agents, since 1 and 2 do not have enough amphiphilic character. The structure of the aggregates formed upon transfer of the CD-capped nanoparticles to the chloroform solution is postulated to resemble that of reverse micelles, as the nanoparticles template the peripheral arrangement of the cationic ferrocene amphiphiles, counterions and water molecules around their surfaces.

Production of human type I collagen in yeast reveals unexpected new insights into the molecular assembly of collagen trimers

Substantial evidence supports the role of the procollagen C-propeptide in the initial association of procollagen polypeptides and for triple helix formation. To evaluate the role of the propeptide domains on triple helix formation, human recombinant type I procollagen, pN-collagen (procollagen without the C-propeptides), pC-collagen (procollagen without the N-propeptides), and collagen (minus both propeptide domains) heterotrimers were expressed in Saccharomyces cerevisiae. Deletion of the N- or C-propeptide, or both propeptide domains, from both proalpha-chains resulted in correctly aligned triple helical type I collagen. Protease digestion assays demonstrated folding of the triple helix in the absence of the N- and C-propeptides from both proalpha-chains. This result suggests that sequences required for folding of the triple helix are located in the helical/telopeptide domains of the collagen molecule. Using a strain that does not contain prolyl hydroxylase, the same folding mechanism was shown to be operative in the absence of prolyl hydroxylase. Normal collagen fibrils were generated showing the characteristic banding pattern using this recombinant collagen. This system offers new opportunities for the study of collagen expression and maturation.

Cytonic acids A and B: novel tridepside inhibitors of hCMV protease from the endophytic fungus Cytonaema species

Two novel human cytomegalovirus protease inhibitors, cytonic acids A (1) and B (2), have been isolated from the solid-state fermentation of the endophytic fungi Cytonaema sp. Their structures as p-tridepside isomers were elucidated by MS and NMR methods.