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Combined low-intensity pulsed ultrasound and extracorporeal shock wave therapy reduces pain and inflammation in knee osteoarthritis patients

Abstract Knee Osteoarthritis (KOA) is a degenerative joint condition that leads to pain and limited mobility. Non-invasive treatments like Low-Intensity Pulsed Ultrasound (LIPUS) and Extracorporeal Shockwave Therapy (ESWT) help manage symptoms and support recovery. While both methods are effective, no studies have directly compared ESWT alone to its combination with LIPUS (LESWT, low-intensity pulsed ultrasound + ESWT) for KOA. This study aims to assess their efficacy and provide evidence for treatment choices. The study included 110 patients with KOA who underwent LESWT, forming the LESWT group, and another 110 KOA patients who were treated with ESWT, constituting the ESWT group. Evaluations were conducted to compare clinical outcomes, levels of inflammatory markers in joint synovial fluid, and the occurrence of adverse events before and after the treatment. The LESWT group showed a higher clinical effective rate (87.3%) compared to the ESWT group (73.6%, p  < 0.01), with greater improvements in LKSS, Lequesne index, VAS, WOMAC, and ROM scores ( p  < 0.05). Levels of inflammatory markers (NO, IL-1β, TNF-α, MMP-3) declined, whereas SOD and TGF-β1 levels rose, with the LESWT group exhibiting more pronounced changes ( p  < 0.01). The occurrence of adverse events showed no significant difference between the groups ( p  > 0.05). LESWT demonstrates significant efficacy in alleviating pain and reducing inflammatory markers in patients with KOA, making it a promising therapeutic option deserving of further clinical consideration. Trial registration : The study protocol was registered on Chinese Clinical Trial Registry, ChiCTR2457249805. Registered 03/08/2022, https://www.chictr.org.cn/ .

Combined effects of red light and direct-current electric fields on neurite growth in 3D neural cell cultures

Interpreting epidemiological surveillance data: a modelling study based on Pune city

Abstract Routine epidemiological surveillance data represents one of the most continuous and current sources of data during the course of an epidemic. This data is used to calibrate epidemiological forecasting models, as well as for public health decision making such as the imposition and lifting of lockdowns and quarantine measures. However, such data is generated during testing and contact tracing and not through randomized sampling. Furthermore, since the process of generating this data affects the epidemic trajectory itself – identification of infected persons might lead to them being quarantined, for instance – it is unclear how representative such data is of the actual epidemic itself. For example, will the observed rise in infections correspond well with the actual rise in infections? To answer such questions, we employ epidemiological simulations not to study the effectiveness of different public health strategies in controlling the spread of the epidemic , but to study the quality of the resulting surveillance data and derived metrics and their utility for decision making. Using the BharatSim simulation framework, we build an agent-based epidemiological model with a detailed representation of testing and contact tracing strategies based on those employed in Pune city during the COVID-19 pandemic to generate synthetic surveillance data. Infected persons are identified, quarantined and/or hospitalised based on these strategies. We perform extensive simulations to study the impact of different public health strategies and the availability of tests and contact tracing efficiencies on the resulting surveillance data as well as on the course of the epidemic. The fidelity of the resulting surveillance data in representing the real-time state of the epidemic and in decision-making is explored in the context of Pune city.

Hybrid learning framework for synergistic fusion of SAR and optical UAV data in wildfire surveillance

Monitoring wheat leaf rust severity using machine learning techniques

Tunable optoelectronic properties of reduced graphene oxide superlattices intercalated with poly(2-amino-1-mercaptobenzene) for Van der Waals photonic heterostructures

In silico evaluation of the anti Helicobacter pylori activity of selected bioactive compounds from Aloe vera

Comparing activated carbon and graphene-based electrodes using electrosorption process to quantify environmental impact associated with thorium extraction via LCA framework

Dual-drug codelivery gelatin-based hydrogel of ARV-471 and Palbociclib enhances synergistic effect in breast cancer treatment

Exploring artificial intelligence chatbots in pediatric fluoride education: a cross-sectional study

Multi-omics integration of transcriptome, miRNA, and metabolome uncovers molecular mechanisms of male flower development in cucumber line B10 (Cucumis sativus L.)

Abstract Male flower development in cucumber ( Cucumis sativus L.) is a highly coordinated and genetically regulated process, yet the full complexity of its molecular underpinnings remains incompletely understood. In this study, we present a comprehensive, multi-omics analysis of male flower development in the cucumber line B10, integrating transcriptomic (RNA-seq), small RNA (miRNA) profiling, and metabolomic data across key tissues, including leaves, shoot apex, and floral buds at distinct developmental stages. Our analyses reveal dynamic gene expression changes and novel regulatory miRNAs, several of which have not previously been linked to male bud formation in cucumber. Functional enrichment analyses using GO and KEGG highlight critical pathways, including starch and sucrose metabolism, carbohydrate utilization, sporopollenin biosynthesis, and lignin catabolism. An integrative analysis combining miRNA–target interactions, transcriptomic shifts, and differential metabolite accumulation revealed coherent regulatory cascades linking transcription factors, carbohydrate metabolism, and cell wall dynamics. This study provides novel insights into the intricate genetic and metabolic networks shaping male flower morphogenesis and provides a valuable resource for advancing cucumber reproductive biology and crop improvement strategies.

A multicenter study on clinico-epidemiological profile of phenylketonuria in Egyptian children

Abstract Phenylketonuria is the most common heritable metabolic disorder. Early detection through newborn screening and proper nutritional management are essential for preventing neurodevelopmental complications. This study aims to describe the epidemiological profile of PKU in Egypt, assess the impact of early diagnosis, and examine the relationship between dietary adherence and comorbidities, including developmental and growth impairment. This is a multicenter retrospective cross-sectional study conducted in four university hospitals in Egypt between January 2024 and January 2025. A total of 365 patients with PKU aged 0–18 years were included. Data on demographics, phenotype classification, complications, and diet adherence were collected. We found that the most common PKU phenotype was classic PKU (36.3%). Early diagnosis through NBS was reported in 67.7%, and dietary adherence in 79.5%. Developmental delay was significantly lower in early-diagnosed children (3.2%) than in late-diagnosed children (100%). BH4 deficiency (1.6%) was associated with developmental delay and epilepsy despite early diagnosis. Diet adherence was linked to lower phenylalanine levels and fewer complications. Neurodevelopmental problems in PKU were decreased by the national NBS program. Better results depend on early diagnosis, diet adherence, and awareness of BH4 deficiency. Diet non-adherence not only worsens neurodevelopmental outcomes but also negatively affects growth parameters in these children.

A smart manufacturing paradigm for robotic welding process optimization through machine learning

Evaluation of primary care mental health integration and screening of anxiety and depression symptoms in Qatar from 2018 to 2023

A vision transformer model-integrated mobile application for early and accurate detection of lumpy skin disease in cattle

Large language models robustness against perturbation

A meta-analytic analysis of the acute effects of MDMA on empathy and emotion recognition in humans

Abstract 3,4-methylenedioxymethamphetamine (MDMA) is an amphetamine derivative known as an “entactogen,” influencing emotional and social processing. Phase III clinical trials of MDMA-assisted psychotherapy for post-traumatic stress disorder have reported promising results, and MDMA-assisted therapy is currently under regulatory review. However, the precise mechanisms underlying positive treatment outcomes remain largely unknown. This meta-analysis aims to systematically synthesize existing data on the effects of MDMA on empathy and emotion recognition. Specifically, we focus on two established tasks, the Multifaceted Empathy Test (MET) and the Facial Emotion Recognition Task (FERT), to comprehensively evaluate MDMA’s role in social cognition. Separate meta-analyses for each emotion tested in the FERT will help discern potential variations in MDMA’s impact on specific emotional responses. Following PRISMA guidelines, we conducted the meta-analysis. The MET assesses cognitive and emotional empathy, while the FERT measures accuracy in identifying basic emotional expressions. Separate meta-analyses were performed for each emotion tested in the FERT. MDMA administration enhances emotional empathy but diminishes recognition accuracy of negative facial expressions (sadness, fear, anger). No significant effects on cognitive empathy or recognition of happy expressions were found. Understanding these nuanced effects may inform the optimization of therapeutic applications and considerations for safety in clinical settings. Further studies are warranted to elucidate the underlying psychological and neural mechanisms, emphasizing the importance of continued investigation into the multifaceted influence of MDMA on social cognition.

Sensor fault diagnosis strategy based on rotor flux observers in three-phase induction motor drive

Emergent multiscale dynamics in photonic neurons with dual feedback

Synthetic hydroxyapatite: a perfect substitute for dental enamel in biofilm formation studies

Abstract In contact with saliva, tooth enamel is covered by biomolecules forming an initial biofilm. Microorganisms attach to the initial biofilm and form the bacterial biofilm, which can provoke diseases. Therefore, dental biofilms are the focus of preventive research. Enamel consists mainly of hydroxyapatite (HAP). Yet, the composition of dental apatite differs between individuals and influences enamel properties. Standardized surfaces might therefore be useful for biofilm research. Synthetic enamel-like HAP pellets perfectly meet the criteria for such well-defined samples. However, systematic investigations of synthetic HAP on oral biofilm formation have never been performed, especially not in comparison to enamel. Therefore, we systematically compared the in situ biofilm formation on synthetic HAP and enamel to investigate the suitability of HAP as a substitute for natural enamel in biofilm formation studies. We observed no differences in formation kinetics, microstructure and subject-specificity of the initial biofilm on both materials. Furthermore, at the proteome level the development of the biofilm on HAP follows the formation patterns observed for enamel. Formation kinetics and morphology of the bacterial biofilm were also subject-dependent and not distinguishable between the two materials. However, the bacterial viability on HAP was higher than on enamel. For bacterial biofilm viability studies, synthetic HAP may therefore even be the preferred substrate as it is more beneficial for identifying antimicrobial agents. In summary, the results prove synthetic HAP as perfect substrate for dental biofilm studies.