Therapeutic options for patients with rare rheumatic diseases: a systematic review and meta-analysis

A diagnostic support system based on pain drawings: binary and k-disease classification of EDS, GBS, FSHD, PROMM, and a control group with Pain2D

BACKGROUND AND OBJECTIVE

The diagnosis of rare diseases (RDs) is often challenging due to their rarity, variability and the high number of individual RDs, resulting in a delay in diagnosis with adverse effects for patients and healthcare systems. The development of computer assisted diagnostic decision support systems could help to improve these problems by supporting differential diagnosis and by prompting physicians to initiate the right diagnostic tests. Towards this end, we developed, trained and tested a machine learning model implemented as part of the software called Pain2D to classify four rare diseases (EDS, GBS, FSHD and PROMM), as well as a control group of unspecific chronic pain, from pen-and-paper pain drawings filled in by patients.

METHODS

Pain drawings (PDs) were collected from patients suffering from one of the four RDs, or from unspecific chronic pain. The latter PDs were used as an outgroup in order to test how Pain2D handles more common pain causes. A total of 262 (59 EDS, 29 GBS, 35 FSHD, 89 PROMM, 50 unspecific chronic pain) PDs were collected and used to generate disease specific pain profiles. PDs were then classified by Pain2D in a leave-one-out-cross-validation approach.

RESULTS

Pain2D was able to classify the four rare diseases with an accuracy of 61-77% with its binary classifier. EDS, GBS and FSHD were classified correctly by the Pain2D k-disease classifier with sensitivities between 63 and 86% and specificities between 81 and 89%. For PROMM, the k-disease classifier achieved a sensitivity of 51% and specificity of 90%.

CONCLUSIONS

Pain2D is a scalable, open-source tool that could potentially be trained for all diseases presenting with pain.

Discovery of a novel antibacterial protein CB6-C to target methicillin-resistant Staphylococcus aureus

Given a serious threat of multidrug-resistant bacterial pathogens to global healthcare, there is an urgent need to find effective antibacterial compounds to treat drug-resistant bacterial infections. In our previous studies, Bacillus velezensis CB6 with broad-spectrum antibacterial activity was obtained from the soil of Changbaishan, China. In this study, with methicillin-resistant Staphylococcus aureus as an indicator bacterium, an antibacterial protein was purified by ammonium sulfate precipitation, Sephadex G-75 column, QAE-Sephadex A 25 column and RP-HPLC, which demonstrated a molecular weight of 31.405 kDa by SDS-PAGE. LC–MS/MS analysis indicated that the compound was an antibacterial protein CB6-C, which had 88.5% identity with chitosanase (Csn) produced by Bacillus subtilis 168. An antibacterial protein CB6-C showed an effective antimicrobial activity against gram-positive bacteria (in particular, the MIC for MRSA was 16 μg/mL), low toxicity, thermostability, stability in different organic reagents and pH values, and an additive effect with conventionally used antibiotics. Mechanistic studies showed that an antibacterial protein CB6-C exerted anti-MRSA activity through destruction of lipoteichoic acid (LTA) on the cell wall. In addition, an antibacterial protein CB6-C was efficient in preventing MRSA infections in in vivo models. In conclusion, this protein CB6-C is a newly discovered antibacterial protein and has the potential to become an effective antibacterial agent due to its high therapeutic index, safety, nontoxicity and great stability.