Comparing bone resorption after anatomical shoulder arthroplasty between various surgical procedures using a single-stem model

BackgroundIn shoulder arthroplasty, bone resorption around the stem can lead to stem loosening and makes surgery difficult at the time of revision. Proximal bone resorption after reverse shoulder arthroplasty can cause instability because of a decrease of deltoid wrapping effect. As factors of the stem itself, such as stem coating, shape, length, and use of bone cement, may also affect bone resorption, a single-stem model should be used to compare bone resorptions between different pathologies and surgical procedures. However, to date, a few reports have compared these differences in detail using a single-stem model. Therefore, we investigated the prevalence and location of humeral bone resorption in a single-stem model.MethodsThe study included 100 shoulders that underwent anatomical total shoulder arthroplasty (TSA) or humeral head replacement (HHR) with a single uncemented humeral stem from 2008 to 2018. The patients were 31 men and 69 women. The mean age at surgery was 72.9 years (range, 41-86 years). The patients were divided into three groups: especially, 25, 61, and 14 shoulders received TSA for primary osteoarthritis without rotator cuff tears (TSA group), HHR using an anatomical head with rotator cuff repair for cuff tear arthropathy (CTA) (HHR group), and HHR using a CTA head without rotator cuff repair (CTA group), respectively. Patients were monitored for a mean of 56 months (range, 12-98 months). The location of bone resorption was divided into seven zones as follows: zone 1, greater tuberosity; zone 2, lateral diaphysis; zone 3, lateral diaphysis beyond the deltoid tuberosity; zone 4, tip of the stem; zone 5, medial diaphysis beyond the deltoid tuberosity; zone 6, medial diaphysis; and zone 7, calcar region. The degree of bone resorption was classified from grade 0 to 4.ResultsBone resorption of grade 3 or higher was significantly more frequent at the greater tuberosity in the HHR and CTA groups (P < .001 and P < .001, respectively) than that in the TSA group. Grade 4 bone resorption was significantly more frequent in the CTA than that in the TSA and HHR groups in zone 1 (P = .016 and P = .041, respectively).ConclusionThe state of attachment of the rotator cuff to the greater tuberosity might affect bone resorption at the greater tuberosity, such as the greater tuberosity after shoulder arthroplasty. In cases of shoulder arthroplasty for arthropathy with rotator cuff tear, performing rotator cuff repair might prevent bone resorption.Level of evidenceLevel IV; Prognosis Study     

Capsule endoscopy for small‐intestinal disorders: Current status

Small‐bowel capsule endoscopy (SBCE) is used widely because of its non‐invasive and patient‐friendly nature. SBCE can visualize entire small‐intestinal mucosa and facilitate detection of small‐intestinal abnormalities. In this review article, we focus on the current status of SBCE. Several platforms for SBCE are available worldwide. Third‐generation SBCE (PillCam^® SB3) has a high‐resolution camera equipped with an adaptive frame rate system. Several software modes have been developed to reduce the reading time for capsule endoscopy and to minimize the possibility of missing lesions. The main complication of SBCE is capsule retention. Thus, the main contraindication for SBCE is known or suspected gastrointestinal obstruction unless intestinal patency is proven. Possible indications for SBCE are obscure gastrointestinal bleeding, Crohn's disease, small‐intestinal polyps and tumors, and celiac disease. Colon capsule endoscopy (CCE) can observe inflamed colonic mucosa non‐invasively, and allows for the continuous and non‐invasive observation of the entire intestinal tract (pan‐endoscopy). Recently, application of CCE as pan‐enteric endoscopy for inflammatory bowel diseases (including Crohn's disease) has been reported. In the near future, reading for CE will be assisted by artificial intelligence, and reading CE videos for long periods will not be required.     

MicroRNA‐361‐3p is a potent therapeutic target for oral squamous cell carcinoma

MicroRNAs (miRNAs) can act not only as tumor suppressor genes but also as oncogenes. Oncogenic miRNAs (oncomiRs) could therefore provide opportunities for the treatment of human malignancies. Here, we aimed to identify oncomiRs present in oral squamous cell carcinoma (OSCC) and addressed whether targeting these miRNAs might be useful in treatment for cancer. Functional screening for oncomiRs in a human OSCC cell line (GFP‐SAS) was carried out using the miRCURY LNA microRNA Knockdown Library – Human version 12.0. We identified a locked nucleic acid (LNA)/DNA antisense oligonucleotide against miR‐361‐3p (LNA‐miR‐361‐3p) which showed the largest degree of growth inhibition of GFP‐SAS cells. Transfection with a synthetic mimic of mature miR‐361‐3p resulted in an approximately 20% increase in the growth of GFP‐SAS cells. We identified odd‐skipped related 2 (OSR2) as a miR‐361‐3p target gene. Transfection of GFP‐SAS cells with LNA‐miR‐361‐3p caused a significant increase in the expression levels of OSR2. Cotransfection of a OSR2 3′‐UTR luciferase reporter plasmid and LNA‐miR‐361‐3p into GFP‐SAS cells produced higher levels of luciferase activity than in cells cotransfected with the LNA‐nontarget. We assessed the effect of LNA‐miR‐361‐3p on the in vivo growth of GFP‐SAS cells. We found that LNA‐miR‐361‐3p significantly reduced the size of s.c. xenografted GFP‐SAS tumors, compared to the control group treated with LNA‐NT. Finally, we observed that miR‐361‐3p is overexpressed in OSCC tissues. These results suggest that miR‐361‐3p supports the growth of human OSCC cells both in vitro and in vivo and that targeting miR‐361‐3p could be a useful therapeutic approach for patients with OSCC.     

Stratification of HPV-associated and HPV-negative oropharyngeal squamous cell carcinomas based on DNA methylation epigenotypes

While the incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been increasing in these two decades, primarily due to human papillomavirus (HPV), stratification of OPSCC into molecular subgroups showing different clinicopathological features has not been fully investigated. We performed DNA methylome analysis using Infinium 450k for 170 OPSCC cases, including 89 cases in our cohort and 81 cases reported by The Cancer Genome Atlas, together with targeted exon sequencing analysis. We stratified OPSCC by hierarchical clustering analysis using methylome data. Methylation levels of classifier markers were validated quantitatively using pyrosequencing, and area under the curve (AUC) values of receiver operating characteristics (ROC) curves were calculated. OPSCC was stratified into four epigenotypes: HPV(+) high-methylation (OP1), HPV(+) intermediate-methylation (OP2), HPV(−) intermediate-methylation (OP3) and HPV(−) low-methylation (OP4). Ten methylation marker genes were generated: five to classify HPV(+) cases into OP1 and OP2, and five to classify HPV(−) cases into OP3 and OP4. AUC values of ROC curves were 0.969 and 0.952 for the two marker panels, respectively. While significantly higher TP53 mutation and CCND1 copy number gains were observed in HPV(−) than in HPV(+) groups (p < 0.01), no significant difference of genomic aberrations was observed between OP1 and OP2, or OP3 and OP4. The four epigenotypes showed significantly different prognosis (p = 0.0006), distinguishing the most favorable OPSCC subgroup (OP1) among generally favorable HPV(+) cases, and the most unfavorable OPSCC subgroup (OP3) among generally unfavorable HPV(−) cases. HPV(+) and HPV(−) OPSCC are further divided into distinct DNA methylation epigenotypes, showing significantly different prognosis.     

Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma

Using surgically resected tissue, we identified characteristic metabolites related to the diagnosis and malignant status of clear cell renal cell carcinoma (ccRCC). Specifically, we quantified these metabolites in urine samples to evaluate their potential as clinically useful noninvasive biomarkers of ccRCC. Between January 2016 and August 2018, we collected urine samples from 87 patients who had pathologically diagnosed ccRCC and from 60 controls who were patients with benign urological conditions. Metabolite concentrations in urine samples were investigated using liquid chromatography‐mass spectrometry with an internal standard and adjustment based on urinary creatinine levels. We analyzed the association between metabolite concentration and predictability of diagnosis and of malignant status by multiple logistic regression and receiver operating characteristic (ROC) curves to establish ccRCC predictive models. Of the 47 metabolites identified in our previous study, we quantified 33 metabolites in the urine samples. Multiple logistic regression analysis revealed 5 metabolites (l ‐glutamic acid, lactate, d ‐sedoheptulose 7‐phosphate, 2‐hydroxyglutarate, and myoinositol) for a diagnostic predictive model and 4 metabolites (l ‐kynurenine, l ‐glutamine, fructose 6‐phosphate, and butyrylcarnitine) for a predictive model for clinical stage III/IV. The sensitivity and specificity of the diagnostic predictive model were 93.1% and 95.0%, respectively, yielding an area under the ROC curve (AUC) of 0.966. The sensitivity and specificity of the predictive model for clinical stage were 88.5% and 75.4%, respectively, with an AUC of 0.837. In conclusion, quantitative analysis of urinary metabolites yielded predictive models for diagnosis and malignant status of ccRCC. Urinary metabolites have the potential to be clinically useful noninvasive biomarkers of ccRCC to improve patient outcomes.