sInterBase: a comprehensive database of Escherichia coli sRNA-mRNA interactions.

SUMMARY: sInterBase is a comprehensive and easy-to-operate web-based platform for mining experimentally identified sRNA-mRNA interactions in Escherichia coli. Interactions in the database are annotated with an interaction duplex and a set of descriptive features. sInterBase provides advanced functionality, such as flexible search based on various criteria, statistical analysis via charts, browsing, and downloading interactions for further use. AVAILABILITY AND IMPLEMENTATION: sInterBase is available at https://sinterbase.cs.bgu.ac.il/.

minMLST: machine learning for optimization of bacterial strain typing.

MOTIVATION: High-resolution microbial strain typing is essential for various clinical purposes, including disease outbreak investigation, tracking of microbial transmission events and epidemiological surveillance of bacterial infections. The widely used approach for multilocus sequence typing (MLST) that is based on the core genome, cgMLST, has the advantage of a high level of typeability and maximal discriminatory power. Yet, the transition from a seven loci-based scheme to cgMLST involves several challenges, that include the need by some users to maintain backward compatibility, growing difficulties in the day-to-day communication within the microbiology community with respect to nomenclature and ontology, issues with typeability, especially if a more stringent approach to loci presence is used, and computational requirements concerning laboratory data management and sharing with end-users. Hence, methods for optimizing cgMLST schemes through careful reduction of the number of loci are expected to be beneficial for practical needs in different settings. RESULTS: We present a new machine learning-based methodology, minMLST, for minimizing the number of genes in cgMLST schemes by identifying subsets of informative genes and analyzing the trade-off between gene reduction and typing performance. The results achieved with minMLST over eight bacterial species show that despite the reduction in the number of genes up to a factor of 10, the typing performance remains very high and significant with an Adjusted Rand Index that ranges between 0.4 and 0.93 in different species and a P-value < 10-3. The identification of such optimized MLST schemes for bacterial strain typing is expected to improve the implementation of cgMLST by improving interlaboratory agreement and communication. AVAILABILITY AND IMPLEMENTATION: The python package minMLST is available at https://PyPi.org/project/minmlst/PyPI and supported on Linux and Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterizing patient compliance over six months in remote digital trials of Parkinson's and Huntington disease.

BACKGROUND: A growing number of clinical trials use various sensors and smartphone applications to collect data outside of the clinic or hospital, raising the question to what extent patients comply with the unique requirements of remote study protocols. Compliance is particularly important in conditions where patients are motorically and cognitively impaired. Here, we sought to understand patient compliance in digital trials of two such pathologies, Parkinson's disease (PD) and Huntington disease (HD). METHODS: Patient compliance was assessed in two remote, six-month clinical trials of PD (n = 51, Clinician Input Study funded by the Michael J. Fox Foundation for Parkinson's Research) and HD (n = 17, sponsored by Teva Pharmaceuticals). We monitored four compliance metrics specific to remote studies: smartphone app-based medication reporting, app-based symptoms reporting, the duration of smartwatch data streaming except while charging, and the performance of structured motor tasks at home. RESULTS: While compliance over time differed between the PD and HD studies, both studies maintained high compliance levels for their entire six month duration. None (- 1%) to a 30% reduction in compliance rate was registered for HD patients, and a reduction of 34 to 53% was registered for the PD study. Both studies exhibited marked changes in compliance rates during the initial days of enrollment. Interestingly, daily smartwatch data streaming patterns were similar, peaking around noon, dropping sharply in the late evening hours around 8 pm, and having a mean of 8.6 daily streaming hours for the PD study and 10.5 h for the HD study. Individual patients tended to have either high or low compliance across all compliance metrics as measured by pairwise correlation. Encouragingly, predefined schedules and app-based reminders fulfilled their intended effect on the timing of medication intake reporting and performance of structured motor tasks at home. CONCLUSIONS: Our findings suggest that maintaining compliance over long durations is feasible, promote the use of predefined app-based reminders, and highlight the importance of patient selection as highly compliant patients typically have a higher adherence rate across the different aspects of the protocol. Overall, these data can serve as a reference point for the design of upcoming remote digital studies. TRIAL REGISTRATION: Trials described in this study include a sub-study of the Open PRIDE-HD Huntington's disease study (TV7820-CNS-20016), which was registered on July 7th, 2015, sponsored by Teva Pharmaceuticals Ltd., and registered on Clinicaltrials.gov as NCT02494778 and EudraCT as 2015-000904-24 .

Self-report symptom-based endometriosis prediction using machine learning.

Endometriosis is a chronic gynecological condition that affects 5-10% of reproductive age women. Nonetheless, the average time-to-diagnosis is usually between 6 and 10 years from the onset of symptoms. To shorten time-to-diagnosis, many studies have developed non-invasive screening tools. However, most of these studies have focused on data obtained from women who had/were planned for laparoscopy surgery, that is, women who were near the end of the diagnostic process. In contrast, our study aimed to develop a self-diagnostic tool that predicts the likelihood of endometriosis based only on experienced symptoms, which can be used in early stages of symptom onset. We applied machine learning to train endometriosis prediction models on data obtained via questionnaires from two groups of women: women who were diagnosed with endometriosis and women who were not diagnosed. The best performing model had AUC of 0.94, sensitivity of 0.93, and specificity of 0.95. The model is intended to be incorporated into a website as a self-diagnostic tool and is expected to shorten time-to-diagnosis by referring women with a high likelihood of having endometriosis to further examination. We also report the importance and effectiveness of different symptoms in predicting endometriosis.

Effect of irradiation on cytokine production in cancer patients.

BACKGROUND: Irradiation, which affects cytokine secretion, is used to treat cancer patients. Cytokine levels have correlations to disease parameters, serving as biomarkers for patients. We aim to explore the effect of irradiation on cytokine production both in vitro (using lymphocytes from healthy donors) and in vivo (using serum levels of head and neck cancer patients following irradiation) and correlating them to mucositis severity/need for percutaneous endoscopic gastroscopy (PEG) tube installation. METHODS: Cytokine production by cultured lymphocytes from healthy donors, in vitro, following irradiation of 5 or 10 Gy. In addition, blood from 23 patients with head and neck cancers, irradiated by 60-72G in vivo, were assessed for inflammatory cytokines (tumor necrosis factor (TNF)α, interleukin (IL)-6, IL-8, IL-18), the anti-inflammatory cytokine IL-10, and the general marker sIL-2R. Following radiation, selected patients who were developing mucositis were treated by PEG tube installation. Changes in cytokine levels were studied as predictive biomarkers of response to therapy/PEG tube installation. Cytokine production levels were measured using ELISAs kits. RESULTS: Irradiation decreased the levels of all tested cytokines, most notably IL-6 and IL-8, proportional to irradiation dose. In patients, increases in cytokine levels, correlated with mucositis severity and potentially the need for PEG tube installation. CONCLUSIONS: Irradiation decreased the levels of all cytokines of healthy lymphocytes in a dose-dependent manner, especially those of IL-6 and IL-8. This study shows a correlation between high and increasing levels of inflammatory cytokines, sIL-2R, plus radiation toxicity and the need for PEG. The reduction of cytokine levels after radiotherapy predicts that PEG will not be required. Thus, our study shows that cytokine changes are predictive biomarkers in head and neck cancer patients.

In vivo restoration of dystrophin expression in mdx mice using intra-muscular and intra-arterial injections of hydrogel microsphere carriers of exon skipping antisense oligonucleotides.

Duchenne muscular dystrophy (DMD) is a genetic disease caused by a mutation in the X-linked Dytrophin gene preventing the expression of the functional protein. Exon skipping therapy using antisense oligonucleotides (AONs) is a promising therapeutic strategy for DMD. While benefits of AON therapy have been demonstrated, some challenges remain before this strategy can be applied more comprehensively to DMD patients. These include instability of AONs due to low nuclease resistance and poor tissue uptake. Delivery systems have been examined to improve the availability and stability of oligonucleotide drugs, including polymeric carriers. Previously, we showed the potential of a hydrogel-based polymeric carrier in the form of injectable PEG-fibrinogen (PF) microspheres for delivery of chemically modified 2'-O-methyl phosphorothioate (2OMePs) AONs. The PF microspheres proved to be cytocompatible and provided sustained release of the AONs for several weeks, causing increased cellular uptake in mdx dystrophic mouse cells. Here, we further investigated this delivery strategy by examining in vivo efficacy of this approach. The 2OMePS/PEI polyplexes loaded in PF microspheres were delivered by intramuscular (IM) or intra-femoral (IF) injections. We examined the carrier biodegradation profiles, AON uptake efficiency, dystrophin restoration, and muscle histopathology. Both administration routes enhanced dystrophin restoration and improved the histopathology of the mdx mice muscles. The IF administration of the microspheres improved the efficacy of the 2OMePS AONs over the IM administration. This was demonstrated by a higher exon skipping percentage and a smaller percentage of centered nucleus fibers (CNF) found in H&E-stained muscles. The restoration of dystrophin expression found for both IM and IF treatments revealed a reduced dystrophic phenotype of the treated muscles. The study concludes that injectable PF microspheres can be used as a carrier system to improve the overall therapeutic outcomes of exon skipping-based therapy for treating DMD.

SP-D loaded PLGA nanoparticles as drug delivery system for prevention and treatment of premature infant's lung diseases.

Preterm infants, particularly those who born between 23 and 28 weeks' gestation, suffer from a very high incidence of respiratory distress syndrome (RDS) related to pulmonary immaturity and inability to make Pulmonary Surfactant (PS). These infants are supported by the use of oxygen, ventilators, and routine administration of surfactant replacement. The currently commercial surfactant replacement therapies do not contain hydrophilic surfactant proteins such as Surfactant Protein D (SP-D). These proteins have a key role in the innate lung host defense, thus the development of a sustained release vehicle that provides SP-D for long periods in preterm infants' lungs would exploit the therapeutic potential of SP-D and other pulmonary medications. The proposed SP-D delivery system is based on nanoparticles (NPs) composed of poly (lactic acid-co-glycolic acid) (PLGA), a biodegradable, FDA approved biopolymer. The resulted NPs were spherical with high Zeta potential value, were not toxic to A-549 lungs cells, and did not induce any inflammatory response in mouse's lungs for short and long-term periods. Moreover, SP-D released from NPs showed biological activity for several days and in vivo release experiment of SP-D loaded NPs revealed that SP-D was released from NPs in mouse lungs with different NPs delivery doses.