An Adaptive EEG Classification Algorithm Based on CSSD and ELM_Kernel for Small Training Samples

Modulation of IFN-γ induced macrophage inflammatory responses via indomethacin-loaded NLCs for OA management

Macrophages are the main cells present in the synovial membrane. They play an important role in the development and progression of osteoarthritis (OA). After the establishment of the disease macrophages mostly adopt a pro-inflammatory secretory phenotype (OA phenotype) further inducing cartilage degradation. Indomethacin (IND) is a non-steroidal anti-inflammatory drug (NSAID) able to inhibit the synthesis of prostaglandins mediated by both cyclooxygenase isoforms depicting a potent anti-inflammatory capacity. However, the lack of specificity and short half-like of free drugs within the joint cavity limits its utility in controlling inflammation after intra-articular administration. This study aims at developing IND loaded glycosylated nanostructured lipid carriers (NLCs) to selectively target macrophages and promote their reprogramming to an anti-inflammatory phenotype. This approach focused on the local administration of the NLCs, offers a promising therapeutic strategy for treating OA by modulating the inflammatory environment within the joint. NLCs will be designed by combining experimental and in silico docking analyses, and thoroughly characterized to obtain drug delivery systems with high stability and suitable physicochemical properties. The proposed mannose-functionalized systems exhibited adequate particle sizes (≈ 70 nm) and positive surface charges (> 20 mV) to be efficiently retained in the joint cavity. Moreover, the developed NLCs demonstrated effective and specific uptake by OA-like macrophages leading to a significant decrease in the secretion of the pro-inflammatory cytokines IL-6, IL-8 and TNF-α similarly to the free drug. Therefore, these systems effectively reprogrammed OA-associated macrophages to adopt a more regenerative phenotype, offering a promising strategy for managing inflammation in OA.

Overnight Sleep Staging Using Chest-Worn Accelerometry

Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform "proxy" sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13-83 years, with BMI 18-47 kg/m2. We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen's kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine.

Directing cellular responses in a nanocomposite 3D matrix for tissue regeneration with nanoparticle-mediated drug delivery

Hydrogels play an important role in tissue engineering due to their native extracellular matrix-like characteristics, but they are insufficient in providing the necessary stimuli to support tissue formation. Efforts to integrate bioactive cues directly into hydrogels are hindered by incompatibility with hydrophobic drugs, issues of burst/uncontrolled release, and rapid degradation of the bioactive molecules. Skeletal muscle tissue repair requires internal stimuli and communication between cells for regeneration, and nanocomposite systems offer to improve the therapeutic effects in tissue regeneration. Here, the versatility of mesoporous silica nanoparticles (MSN) was leveraged to formulate a nanoparticle-hydrogel composite and to combine the benefits of controlled delivery of bioactive cues and cellular support. The tunable surface characteristics of MSNs were exploited to optimize homogeneity and intracellular drug delivery in a 3D matrix. Nanocomposite hydrogels formulated with acetylated or succinylated MSNs achieved high homogeneity in 3D distribution, with succinylated MSNs being rapidly internalized and acetylated MSNs exhibiting slower cellular uptake. MSN-hydrogel nanocomposites simultaneously allowed efficient local intracellular delivery of a hydrophobic model drug. To further study the efficiency of directing cell response, a Notch signaling inhibitor (DAPT) was incorporated into succinylated MSNs and incorporated into the hydrogel. MSN-hydrogel nanocomposites effectively downregulated the Notch signaling target genes, and accelerated and maintained the expression of myogenic markers. The current findings demonstrate a proof-of-concept in effective surface engineering strategies for MSN-based nanocomposites, suited for hydrophobic drug delivery in tissue regeneration with guided cues.

The Use of Superb Microvascular Imaging in Evaluating Rheumatic Diseases: A Systematic Review

Background and Objectives: Superb microvascular imaging is an advanced Doppler algorithm that seems to be useful in detecting low-velocity blood flow without using a contrast agent. Increasing evidence suggests that SMI is a more sensitive tool than conventional Doppler techniques for evaluating rheumatic diseases, especially inflammatory arthritis. We aimed to assess the use of SMI in evaluating joints and extraarticular structures. Materials and Methods: Two reviewers independently reviewed the literature to provide a global overview of the possibilities of SMI in rheumatology. Original English-language articles published between February 2014 and November 2022 were identified through database (PubMed, Medline, Ebsco, the Cochrane Library, and ScienceDirect) searching, and analysed to summarise existing evidence according to PRISMA methodology. Inclusion criteria covered original research articles reporting applications of SMI on rheumatic diseases and musculoskeletal disorders secondary to rheumatic conditions. Qualitative data synthesis was performed. Results: A total of 18 articles were included. No systematic reviews fulfilled our inclusion criteria. Most studies focused on characterising the synovial vascularity of rheumatoid arthritis. There have been several attempts to demonstrate SMI's value for evaluating extra-articular soft tissues (fat pads or salivary glands) and large-diameter vessels. The quantitative importance of SMI vascular indices could become a useful non-invasive diagnostic marker. Studies on therapeutic applications are still scarce, and the majority of studies have gaps in reporting the methodology (ultrasound performance technique and settings) of the research. Conclusions: SMI has proved to be useful in characterising low-flow vascularity, and growing evidence indicates that SMI is a non-invasive and lower-cost tool for prognostic assessment, especially in inflammatory arthritis. Preliminary findings also suggest potential interest in evaluating the effect of treatment.

Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system

To improve the hospital's ability to proactively detect infectious diseases, a knowledge-based infectious disease monitoring and decision support system was established based on real medical records and knowledge rules. The effectiveness of the system was evaluated using interrupted time series analysis. In the system, a monitoring and alert rule library for infectious diseases was generated by combining infectious disease diagnosis guidelines with literature and a real medical record knowledge map. The system was integrated with the electronic medical record system, and doctors were provided with various types of real-time warning prompts when writing medical records. The effectiveness of the system's alerts was analyzed from the perspectives of false positive rates, rule accuracy, alert effectiveness, and missed case rates using interrupted time series analysis. Over a period of 12 months, the system analyzed 4,497,091 medical records, triggering a total of 12,027 monitoring alerts. Of these, 98.43% were clinically effective, while 1.56% were invalid alerts, mainly owing to the relatively rough rules generated by the guidelines leading to several false alarms. In addition, the effectiveness of the system's alerts, distribution of diagnosis times, and reporting efficiency of doctors were analyzed. 89.26% of infectious disease cases could be confirmed and reported by doctors within 5 min of receiving the alert, and 77.6% of doctors could complete the filling of 33 items of information within 2 min, which is a reduction in time compared to the past. The timely reminders from the system reduced the rate of missed cases by doctors; the analysis using interrupted time series method showed an average reduction of 4.4037% in the missed-case rate. This study proposed a knowledge-based infectious disease decision support system based on real medical records and knowledge rules, and its effectiveness was verified. The system improved the management of infectious diseases, increased the reliability of decision-making, and reduced the rate of underreporting.