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Purpose This prospective randomised control study is to demonstrate whether or not there is a clinical benefit from inserting a Wallis implant on the functional recovery of patients who have undergone lumbar decompression surgery. Method Sixty consecutive patients with an average age of 58 years (34–81) who were selected for primary lumbosacral decompression were randomly assigned into two groups with equal number of patients, decompression alone or decompression with Wallis implant. The patients had an average follow-up of 40 months. Patients were assessed by visual analogue scale (VAS) (Boonstra et al., Int J Rehabil Res 31:165–169, 2008 ; Price et al., Pain 17:45–56, 1983 ) pain score for back and leg pain, and the Oswestry Disability Index questionnaire (ODI) (Smeets et al., Arthritis Care Res (Hoboken) 63:S158–S173, 2011 ). Results The results in both the groups did not reveal a significant difference in the clinical outcome assessment of back pain score or ODI. With the Wilcoxon two-sample test, no difference in median values was achieved ( p value 0.0787 for ODI and p value 0.1926 for back pain). The average ODI in the Wallis group dropped from 50.93 to 29.11. The average VAS for the Wallis group back pain dropped from 7.79 to 4.22. Conclusion The Wallis implant is a safe medical device. This study revealed a reduction in pain and functional disability in patients treated with decompression surgery for lumbar stenosis, with or without Wallis. The Wallis group improved more, but it was not statistically significant. The risk of complications is lower than other interspinous devices [ 18 , 19 ].

Evaluating large language models (LLMs) is challenging. Traditional ground-truth-based benchmarks fail to capture the comprehensiveness and nuance of real-world queries, while LLM-as-judge benchmarks suffer from grading biases and limited query quantity. Both of them may also become contaminated over time. User-facing evaluation, such as Chatbot Arena, provides reliable signals but is costly and slow. In this work, we propose MixEval, a new paradigm for establishing efficient, gold-standard LLM evaluation by strategically mixing off-the-shelf benchmarks. It bridges (1) comprehensive and well-distributed real-world user queries and (2) efficient and fairly-graded ground-truth-based benchmarks, by matching queries mined from the web with similar queries from existing benchmarks. Based on MixEval, we further build MixEval-Hard, which offers more room for model improvement. Our benchmarks' advantages lie in (1) a 0.96 model ranking correlation with Chatbot Arena arising from the highly impartial query distribution and grading mechanism, (2) fast, cheap, and reproducible execution (6% of the time and cost of MMLU), and (3) dynamic evaluation enabled by the rapid and stable data update pipeline. We provide extensive meta-evaluation and analysis for our and existing LLM benchmarks to deepen the community's understanding of LLM evaluation and guide future research directions.

Perceiving and generating diverse modalities are crucial for AI models to effectively learn from and engage with real-world signals, necessitating reliable evaluations for their development. We identify two major issues in current evaluations: (1) inconsistent standards, shaped by different communities with varying protocols and maturity levels; and (2) significant query, grading, and generalization biases. To address these, we introduce MixEval-X, the first any-to-any, real-world benchmark designed to optimize and standardize evaluations across diverse input and output modalities. We propose multi-modal benchmark mixture and adaptation-rectification pipelines to reconstruct real-world task distributions, ensuring evaluations generalize effectively to real-world use cases. Extensive meta-evaluations show our approach effectively aligns benchmark samples with real-world task distributions. Meanwhile, MixEval-X's model rankings correlate strongly with that of crowd-sourced real-world evaluations (up to 0.98) while being much more efficient. We provide comprehensive leaderboards to rerank existing models and organizations and offer insights to enhance understanding of multi-modal evaluations and inform future research.

Generative adversarial networks (GANs) have been remarkably successful in learning complex high dimensional real word distributions and generating realistic samples. However, they provide limited control over the generation process. Conditional GANs (cGANs) provide a mechanism to control the generation process by conditioning the output on a user defined input. Although training GANs requires only unsupervised data, training cGANs requires labelled data which can be very expensive to obtain. We propose a framework for semi-supervised training of cGANs which utilizes sparse labels to learn the conditional mapping, and at the same time leverages a large amount of unsupervised data to learn the unconditional distribution. We demonstrate effectiveness of our method on multiple datasets and different conditional tasks.

Glycogen synthase kinase-3 beta (GSK-3β) is a serine/threonine kinase implicated in various diseases such as Alzheimer’s, diabetes, and cancer, making it a pivotal therapeutic target. This study uniquely integrates a curated phytochemical library with comprehensive ADMET filtering and 200 ns molecular dynamics (MD) simulations to identify stable GSK-3β inhibitors, offering deeper mechanistic and dynamic insights than previous docking-based studies. ADMET profiling shortlisted 49 compounds, with uzarigenin, jatrophone, chrysin, and podolide exhibiting superior binding affinities compared to Tideglusib (-8.53 kcal/mol). Among these, jatrophone displayed the highest binding affinity (9.00 kcal/mol), followed by uzarigenin (8.63 kcal/mol) and podolide (8.60 kcal/mol), indicating stronger interactions with GSK-3β. Molecular dynamics simulations confirmed stability for uzarigenin and podolide over 200 nanoseconds, supported by RMSD, SASA, and Rg analyses. Principal component and covariance analyses revealed strong residue interactions in these complexes. KEGG pathway analysis highlighted the role of GSK-3β inhibitors in Alzheimer’s disease, Wnt signaling, and cancer pathways. This study identifies phytochemicals with potential therapeutic applications for neurodegenerative, cancer, and metabolic diseases, warranting further experimental validation.

Ventricular septal myomectomy (VSM) is the primary modality for left ventricular outflow tract gradient reduction in patients with obstructive hypertrophic cardiomyopathy with refractory symptoms. Comprehensive postprocedural data for VSM from a large multicenter registry are sparse. The primary objective of this study was to evaluate postprocedural mortality, complications, length of stay (LOS), and cost of hospitalization after VSM and to further appraise the multivariate predictors of these outcomes. The Healthcare Cost and Utilization Project's Nationwide Inpatient Sample was queried from 1998 through 2010 using International Classification of Diseases, Ninth Revision, procedure codes 37.33 for VSM and 425.1 for hypertrophic cardiomyopathy. The severity of co-morbidities was defined using the Charlson co-morbidity index. Hierarchical mixed-effects models were generated to identify independent multivariate predictors of in-hospital mortality, procedural complications, LOS, and cost of hospitalization. The overall mortality was 5.9%. Almost 9% (8.7%) of patients had postprocedural complete heart block requiring pacemakers. Increasing Charlson co-morbidity index was associated with a higher rate of complications and mortality (odds ratio 2.41, 95% confidence interval 1.17 to 4.98, p = 0.02). The mean cost of hospitalization was $41,715 ± $1,611, while the average LOS was 8.89 ± 0.35 days. Occurrence of any postoperative complication was associated with increased cost of hospitalization (+$33,870, p <0.001) and LOS (+6.08 days, p <0.001). In conclusion, the postoperative mortality rate for VSM was 5.9%; cardiac complications were most common, specifically complete heart block. Age and increasing severity of co-morbidities were predictive of poorer outcomes, while a higher burden of postoperative complications was associated with a higher cost of hospitalization and LOS. •Higher postoperative mortality was found after VSM than reported in recent studies.•Age was predictive of higher postoperative mortality and complications.•Higher burden of co-morbidities predicted higher postoperative mortality and complications.•More postoperative complications were associated with longer LOS.

Cooperativity and antagonism between transcription factors (TFs) can drastically modify their binding to regulatory DNA elements. While mapping these relationships between TFs is important for understanding their context-specific functions, existing approaches either rely on DNA binding motif predictions, interrogate one TF at a time, or study individual TFs in parallel. Here, we introduce paired yeast one-hybrid (pY1H) assays to detect cooperativity and antagonism across hundreds of TF-pairs at DNA regions of interest. We provide evidence that a wide variety of TFs are subject to modulation by other TFs in a DNA region-specific manner. We also demonstrate that TF-TF relationships are often affected by alternative isoform usage and identify cooperativity and antagonism between human TFs and viral proteins from human papillomaviruses, Epstein-Barr virus, and other viruses. Altogether, pY1H assays provide a broadly applicable framework to study how different functional relationships affect protein occupancy at regulatory DNA regions. Combinations of transcription factors (TFs) bind DNA to fine-tune gene expression. Here, the authors map cooperative and antagonistic DNA binding across hundreds of TF-pairs. TF-TF relationships vary depending on DNA targets and TF isoforms.

Background Atogepant is a CGRP receptor antagonist used in prevention of migraine. This study assesses the safety and efficacy of this drug in management of migraine headaches. Methods PubMed, Scopus, Web of Science, and Cochrane CENTRAL were searched until March 24, 2025. Outcomes assessed included monthly migraine and headache day change from baseline at 12 weeks, ≥ 50% reduction in monthly migraine days (MMD), acute medication use days at 12 weeks, treatment-emergent adverse events (TEAE), score on Role Function-Restrictive domain of MSQ at 12 weeks, score on daily activity performance and physical impairment domains of AIM-D at 12 weeks. Subgroup analysis was performed based on different doses of atogepant. Results Six RCTs comprising of 4052 patients were included. Atogepant showed significant improvement in patients with migraine in terms of MMD over 12 weeks at all doses, 10 mg, 30 mg, and 60 mg. Moreover, it also reduced monthly headache days, had 50% reduction in MMD, and reduced days requiring acute medication use. Atogepant was shown to increase the risk of TEAE, particularly gastrointestinal (GI) side effect including constipation and nausea, however, occurrence of other side effects with atogepant use was insignificant. Conclusion Atogepant is a highly effective CGRP antagonist for migraine prevention, however, it is associated with increased incidence of GI side effects. Further studies are needed to comprehensively investigate the relationship between atogepant dosage and migraine improvement and safety profile.

The detection of coronavirus disease (COVID-19) using standard laboratory tests, such as reverse transcription polymerase chain reaction (RT-PCR), is time-consuming. Complex medical imaging problems are currently being solved using machine learning and deep learning techniques. Our proposed solution utilizes chest radiography imaging techniques, which have shown to be a faster alternative for detecting COVID-19. We present an efficient and lightweight deep learning architecture for identifying COVID-19 using chest X-ray images which achieve 99.81% accuracy in intra-database testing and 100% accuracy in cross-validation testing on a separate data set. The results demonstrate the potential of our proposed model as a reliable tool for COVID-19 diagnosis using chest X-ray images, which can have a significant impact on improving the efficiency of COVID-19 diagnosis and treatment.

The increase in the number of carotid artery stenting (CAS) procedures over the last decade has necessitated critical appraisal of procedural outcomes and patterns of utilization including cost analysis. The main objectives of our study were to evaluate the postprocedural mortality and complications after CAS and the patterns of resource utilization in terms of length of stay (LOS) and cost of hospitalization. We queried the Healthcare Cost and Utilization Project's Nationwide Inpatient Sample from 2006 to 2010 using the International Classification of Diseases, Ninth Revision, procedure code of 00.63 for CAS. Hierarchical mixed-effects models were generated to identify the independent multivariate predictors of in-hospital mortality, procedural complications, LOS, and cost of hospitalization. A total of 13,564 CAS procedures (weighted n = 67,344) were analyzed. The overall postprocedural mortality was low at 0.5%, whereas the complication rate was 8%, both of which remained relatively steady over the time frame of the study. Greater postoperative mortality and complications were noted in symptomatic patients, women, and those with greater burden of baseline co-morbidities. A greater operator volume was associated with a lower rate of postoperative mortality and complications, as well as shorter LOS and lesser hospitalization costs. In conclusion, the postprocedural mortality after CAS has remained low over the recent years. Operator volume is an important predictor of postprocedural outcomes and resource utilization.