Similarities and Differences between Clavicular Bacterial Osteomyelitis and Nonbacterial Osteitis: Comparisons of 327 Reported Cases

Background. Currently, both clavicular bacterial osteomyelitis (BO) and nonbacterial osteitis (NBO) remain not well understood owing to their much lower incidences. This study is aimed at summarizing similarities and differences between clavicular BO and NBO based on comparisons of literature-reported cases. Methods. We searched the PubMed and Embase databases to identify English published literature between January 1st, 1980, and December 31st, 2018. Inclusion criteria were studies evaluating clinical features, diagnosis, and treatment of clavicular BO and NBO, with eligible data for synthesis analysis. Results. Altogether, 129 studies with 327 patients were included. Compared with BO, clavicular NBO favored females ( P < 0.001 ) and age below 20 years ( P < 0.001 ) and mostly presented in a chronic phase (disease term exceeding 2 months) ( P < 0.001 ). Although local pain and swelling were the top two symptoms for both disorders, fever, erythema, and a sinus tract were more frequently found in BO patients ( P < 0.01 ). Although they both favored the medial side, lesions in the clavicular lateral side mostly occurred in BO patients ( P = 0.002 ). However, no significant differences were identified regarding the serological levels of white blood cell count ( P = 0.06 ), erythrocyte sedimentation rate ( P = 0.27 ), or C-reactive protein ( P = 0.33 ) between BO and NBO patients before therapy. Overall, the BO patients achieved a statistically higher cure rate than that of the NBO patients ( P = 0.018 ). Conclusions. Females, age below 20 years, and a long duration of clavicular pain and swelling may imply NBO. While the occurrence of a sinus tract and lesions in the lateral side may be clues of BO, inflammatory biomarkers revealed limited values for differential diagnosis. BO patients could achieve a better efficacy than the NBO patients based on current evidence.

Visualization and Analysis of the Interaction Network of Proteins Associated with Blood-cell targeting Autoimmune Diseases

Blood-cell targeting Autoimmune Diseases (BLADs) are complex diseases that affect blood cell formation or prevent blood cell production. Since these clinical conditions are gathering growing attention, experimental approaches are being used to investigate the mechanisms behind their pathogenesis and to identify proteins associated with them. However, computational approaches have not been utilized extensively in the study of BLADs. This study aims to investigate the interaction network of proteins associated with BLADs (BLAD interactome) and to identify novel associations with other human proteins. The method followed in this study combines information regarding protein-protein interaction network properties and autoimmune disease terms. Proteins with high network scores and statistically significant autoimmune disease term enrichment were obtained and 14 of them were designated as candidate proteins associated with BLADs. Additionally, clustering analysis of the BLAD interactome was used and allowed the detection of 17 proteins that act as “connectors” of different BLADs. We expect our findings to further extend experimental efforts for the investigation of the pathogenesis and the relationships of BLADs.

Simple ClinVar: an interactive web server to explore and retrieve gene and disease variants aggregated in ClinVar database

Clinical genetic testing has exponentially expanded in recent years, leading to an overwhelming amount of patient variants with high variability in pathogenicity and heterogeneous phenotypes. A large part of the variant level data is aggregated in public databases such as ClinVar. However, the ability to explore this rich resource and answer general questions such as ‘How many genes inside ClinVar are associated with a specific disease? or ‘In which part of the protein are patient variants located?’ is limited and requires advanced bioinformatics processing. Here, we present Simple ClinVar (http://simple-clinvar.broadinstitute.org/) a web server application that is able to provide variant, gene and disease level summary statistics based on the entire ClinVar database in a dynamic and user-friendly web-interface. Overall, our web application is able to interactively answer basic questions regarding genetic variation and its known relationships to disease. By typing a disease term of interest, the user can identify in seconds the genes and phenotypes most frequently reported to ClinVar. Subsets of variants can then be further explored, filtered or mapped and visualized in the corresponding protein sequences. Our website will follow ClinVar monthly releases and provide easy access to ClinVar resources to a broader audience including basic and clinical scientists.

Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis (Preprint)

BACKGROUND Tweets can provide broad, real-time perspectives about health and medical diagnoses that can inform disease surveillance in geographic regions. Less is known, however, about how much individuals post about common health conditions or what they post about. OBJECTIVE We sought to collect and analyze tweets from 1 state about high prevalence health conditions and characterize the tweet volume and content. METHODS We collected 408,296,620 tweets originating in Pennsylvania from 2012-2015 and compared the prevalence of 14 common diseases to the frequency of disease mentions on Twitter. We identified and corrected bias induced due to variance in disease term specificity and used the machine learning approach of differential language analysis to determine the content (words and themes) most highly correlated with each disease. RESULTS Common disease terms were included in 226,802 tweets (174,381 tweets after disease term correction). Posts about breast cancer (39,156/174,381 messages, 22.45%; 306,127/12,702,379 prevalence, 2.41%) and diabetes (40,217/174,381 messages, 23.06%; 2,189,890/12,702,379 prevalence, 17.24%) were overrepresented on Twitter relative to disease prevalence, whereas hypertension (17,245/174,381 messages, 9.89%; 4,614,776/12,702,379 prevalence, 36.33%), chronic obstructive pulmonary disease (1648/174,381 messages, 0.95%; 1,083,627/12,702,379 prevalence, 8.53%), and heart disease (13,669/174,381 messages, 7.84%; 2,461,721/12,702,379 prevalence, 19.38%) were underrepresented. The content of messages also varied by disease. Personal experience messages accounted for 12.88% (578/4487) of prostate cancer tweets and 24.17% (4046/16,742) of asthma tweets. Awareness-themed tweets were more often about breast cancer (9139/39,156 messages, 23.34%) than asthma (1040/16,742 messages, 6.21%). Tweets about risk factors were more often about heart disease (1375/13,669 messages, 10.06%) than lymphoma (105/4927 messages, 2.13%). CONCLUSIONS Twitter provides a window into the Web-based visibility of diseases and how the volume of Web-based content about diseases varies by condition. Further, the potential value in tweets is in the rich content they provide about individuals’ perspectives about diseases (eg, personal experiences, awareness, and risk factors) that are not otherwise easily captured through traditional surveys or administrative data.

The proinsulin level in the blood of children with type 1 diabetes mellitus of various duration

Proinsulin content was measured in the serum of 82 children (aged from 3 to 14 years) with type 1 diabetes mellitus of various duration. Three groups of patients characterized by low (54%), normal (42%) and high (4%) levels of this prohormone were recognized. No dependence the proinsulin level on the disease term was found. The serum proinsulin level may be used as a parameter specifying the pathogenesis of type 1 diabetes mellitus.

Discrepancy between reported progressive disease per radiology report and treating oncologist tumor measurements in patients with metastatic renal cell carcinoma (mRCC) receiving sunitinib.

420 Background: The determination of progressive disease (PD) on sunitinib in mRCC by conventional tumor size criteria is often complex. Frequently, radiology reports cite disease progression that does not meet RECIST criteria or does not adequately describe index lesions. Characterization of the frequency and magnitude of this phenomenon has not been previously reported. Methods: The medical records of a subset of mRCC patients treated at The Cleveland Clinic who had received sunitinib for > 12 months were retrospectively reviewed. All Radiology reports from post-baseline scans were reviewed for the presence of text in the body or conclusion of the report consistent with disease progression (specifically the terms ‘progressive’, ‘new’ and/or ‘interval enlargement/worsening’). The date of the report first containing one or more of these terms was recorded, as was the date of RECIST-defined PD determined by the treating Oncologist. Results: Twenty patients were identified in an initial review. Patient characteristics included: 85% male, 100% clear cell histology, 90% prior nephrectomy and 80% with prior systemic therapy. Thirteen patients (65%) had a radiology report citing a progressive disease term prior to treating physician-determined RECIST-defined PD. The median time from first radiology report citing a progressive disease term until RECIST PD per Oncologist measurements was 2.9 months (range, 0 to 12.8 months). Conclusions: There is significant discrepancy between the first mention of disease progression by a Radiology report and when a treating Oncologist measures disease progression per RECIST criteria. This discrepancy could impact patient care by influencing drug discontinuation. These data emphasize the importance of multidisciplinary scan review and tumor measurements by both radiologists and treating physicians. Analysis is ongoing in additional patients.