Latent infections in conversion total hip arthroplasty following internal fixation of femoral neck fractures: a systematic review and meta-analysis of diagnostic methods.

BACKGROUND: Accurate diagnosis of latent infections prior to conversion total hip arthroplasty (THA) following internal fixation of femoral neck fractures is crucial for successful surgical outcomes. This systematic review aimed to provide a comprehensive evaluation of the current literature regarding the diagnosis of latent infections before conversion THA. METHODS: Systematic search of PubMed, EMBASE, and Cochrane (CENTRAL) databases was conducted, and the diagnostic accuracy of various markers and techniques was assessed. The quality of the included studies was evaluated using the QUADAS-2 instrument. RESULTS: Five studies comprising 661 patients were included in the review. Pooled analysis using C-reactive protein (CRP) as a diagnostic marker resulted in a sensitivity and specificity of 72% and 76%, respectively, while using erythrocyte sedimentation rate (ESR) yielded a sensitivity and specificity of 75% and 78%, respectively. Fibrinogen and platelet count showed lower sensitivity and specificity compared to CRP and ESR. The best combined markers were CRP and serum platelet count, with a sensitivity of 76% and specificity of 86% based on one study. CONCLUSION: Our review underscored the limitations and inconsistencies present in current diagnostic methods for latent infections in conversion surgery. Future research needs to focus on standardizing threshold values, exploring the potential of synovial fluid analysis, imaging techniques, and molecular methods, as well as developing tailored diagnostic algorithms. PROSPERO: CRD42023394757.

Recent advances on high-efficiency of microRNAs in different types of lung cancer: a comprehensive review.

Carcinoma of the lung is among the most common types of cancer globally. Concerning its histology, it is categorized as a non-small cell carcinoma (NSCLC) and a small cell cancer (SCLC) subtype. MicroRNAs (miRNAs) are a member of non-coding RNA whose nucleotides range from 19 to 25. They are known to be critical regulators of cancer via epigenetic control of oncogenes expression and by regulating tumor suppressor genes. miRNAs have an essential function in a tumorous microenvironment via modulating cancer cell growth, metastasis, angiogenesis, metabolism, and apoptosis. Moreover, a wide range of information produced via several investigations indicates their tumor-suppressing, oncogenic, diagnostic assessment, and predictive marker functions in different types of lung malignancy. miRNA mimics or anti-miRNAs can be transferred into a lung cancer cell, with possible curative implications. As a result, miRNAs hold promise as targets for lung cancer treatment and detection. In this study, we investigate the different functions of various miRNAs in different types of lung malignancy, which have been achieved in recent years that show the lung cancer-associated regulation of miRNAs expression, concerning their function in lung cancer beginning, development, and resistance to chemotherapy, also the probability to utilize miRNAs as predictive biomarkers for therapy reaction.

RNA secondary structure prediction based on SHAPE data in helix regions.

RNA molecules play important and fundamental roles in biological processes. Frequently, the functional form of single-stranded RNA molecules requires a specific tertiary structure. Classically, RNA structure determination has mostly been accomplished by X-Ray crystallography or Nuclear Magnetic Resonance approaches. These experimental methods are time consuming and expensive. In the past two decades, some computational methods and algorithms have been developed for RNA secondary structure prediction. In these algorithms, minimum free energy is known as the best criterion. However, the results of algorithms show that minimum free energy is not a sufficient criterion to predict RNA secondary structure. These algorithms need some additional knowledge about the structure, which has to be added in the methods. Recently, the information obtained from some experimental data, called SHAPE, can greatly improve the consistency between the native and predicted RNA secondary structure. In this paper, we investigate the influence of SHAPE data on four types of RNA substructures, helices, loops, base pairs from the start and end of helices and two base pairs from the start and end of helices. The results show that SHAPE data in helix regions can improve the prediction. We represent a new method to apply SHAPE data in helix regions for finding RNA secondary structure. Finally, we compare the results of the method on a set of RNAs to predict minimum free energy structure based on considering all SHAPE data and only SHAPE data in helix regions as pseudo free energy and without SHAPE data (without any pseudo free energy). The results show that RNA secondary structure prediction based on considering only SHAPE data in helix regions is more successful than not considering SHAPE data and it provides competitive results in comparison with considering all SHAPE data.

Hamstring Injury Treatments and Management in Athletes: A Systematic Review of the Current Literature.

BACKGROUND: The field of sports medicine presents a varied landscape of research on hamstring injuries in athletes, characterized by inconclusive and sometimes conflicting findings on effective treatment and rehabilitation strategies. This discordance prompted the current systematic investigation. METHODS: Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed for conducting the systematic review. Multiple international bibliometric databases (Scopus, PubMed, Web of Science, and Embase) were searched to identify studies evaluating any treatment option for the management of hamstring injuries in athletes. Eligible studies were appraised for quality using Joanna Briggs Institute and Risk of Bias 2 tools. RESULTS: A total of 30 studies with 1,195 participants were included. Of the reviewed studies, treatments varied from aggressive rehabilitation, platelet-rich plasma (PRP) injections, manual techniques, various exercise protocols to modalities like high-power laser and nonsteroidal anti-inflammatory drugs. Evidence suggested benefits from treatments like extensive muscle lengthening during eccentric actions, progressive agility, and trunk stabilization. PRP injections produced mixed results regarding return to sport and reinjury rates. Stretching exercises, sometimes combined with cryotherapy, showed benefits. CONCLUSION: Treatments for hamstring injuries exhibit varied efficacy. Although rest, ice, compression, and elevation remains essential for acute management, rehabilitation focusing on muscle strengthening and flexibility is crucial. The potential benefits of PRP injections, especially for chronic cases, require more conclusive research. A comprehensive approach, combining evidence-based practices and patient-centric factors, is vital for effective management and recovery. LEVEL OF EVIDENCE: Level IV. See Instructions for Authors for a complete description of levels of evidence.

RNA design using simulated SHAPE data.

It has long been established that in addition to being involved in protein translation, RNA plays essential roles in numerous other cellular processes, including gene regulation and DNA replication. Such roles are known to be dictated by higher-order structures of RNA molecules. It is therefore of prime importance to find an RNA sequence that can fold to acquire a particular function that is desirable for use in pharmaceuticals and basic research. The challenge of finding an RNA sequence for a given structure is known as the RNA design problem. Although there are several algorithms to solve this problem, they mainly consider hard constraints, such as minimum free energy, to evaluate the predicted sequences. Recently, SHAPE data has emerged as a new soft constraint for RNA secondary structure prediction. To take advantage of this new experimental constraint, we report here a new method for accurate design of RNA sequences based on their secondary structures using SHAPE data as pseudo-free energy. We then compare our algorithm with four others: INFO-RNA, ERD, MODENA and RNAifold 2.0. Our algorithm precisely predicts 26 out of 29 new sequences for the structures extracted from the Rfam dataset, while the other four algorithms predict no more than 22 out of 29. The proposed algorithm is comparable to the above algorithms on RNA-SSD datasets, where they can predict up to 33 appropriate sequences for RNA secondary structures out of 34.