Browse Articles

Combined treatment using bismuth sulfide nanoparticles loaded with NANOG decoy oligodeoxynucleotides under X-ray radiation for breast cancer cells

A systematic review and meta-analysis on the efficacy of postoperative radiotherapy after gross total resection of intracranial solitary fibrous tumors

Multifunctional nano-delivery system based on DNase I and photodynamic therapy for combatting enterococcus faecalis biofilm infections

Pyrenees as the southernmost European refugium of glacial relict land snails

Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma

An electrochemical approach for tryptophan detection that regulates the kynurenine pathway

Bodily maps of exercise-induced feelings

Potential utility of the triglyceride-glucose index as a predictor of erectile dysfunction and its severity: a cross-sectional, case–control study

XBP1 promotes endometrial fibrosis through cGAS-STING signaling pathway in intrauterine adhesion

New insight into viscosity prediction of imidazolium-based ionic liquids and their mixtures with machine learning models

Virtual reality-based training to augment recovery of hand dexterity after surgery for degenerative cervical myelopathy

Abstract Degenerative cervical myelopathy (DCM), the leading cause of non-traumatic spinal cord injury, frequently results in impaired hand dexterity. While surgical decompression is the primary treatment, over 40% of patients report residual hand disability after surgery. There are no therapies to restore hand function after surgery for DCM. In this single-arm clinical trial, post-surgical DCM participants (within 12 months after surgery) underwent a 4-week VR training protocol using the Virtual Keyboard system, which promotes practice of finger individuation. Assessments of hand dexterity were performed at baseline (at week 1), post-training (at week 6) and follow-up (at week 10). The primary outcome measure for hand dexterity assessment was the Jebsen-Taylor Hand Function Test (JTHFT). Twenty-two post-surgical DCM participants were included in the final analysis. Statistically significant improvement in the JTHFT was observed at both post-training (p < 0.001, Δ= -15.21s) and follow-up (p < 0.001, Δ= -17.84s), with changes exceeding the Minimal Clinically Important Difference (MCID) at both time points. VR hand training also produced significant, sustained and clinically meaningful improvements in quantitative hand dexterity tests and health-related quality of life. The results of this uncontrolled, single-arm study demonstrate the feasibility and efficacy of targeted neurorehabilitation to augment post-surgical neurological recovery in people with DCM.

An efficient multiscale simulation framework integrating dynamic heterogeneity for accurate waterflooding prediction

Development and evaluation of the rivaroxaban loaded nanostructured lipid carriers for improved oral bioavailability and safety

Jackalope Plus tool for post-coordination, ontology development, and precise mapping in observational health studies

An inclusive solution based on grey water footprint for performance evaluation of industrial wastewater treatment systems

Functional significance of commonly regulated genes in mechanically and chemically induced retinal ganglion cell death in rat eyes

Real world pharmacovigilance study of antineoplastic drug related vitiligo risks

Strictosamide and mitraphylline inhibit cancer cell motility by suppressing epithelial-mesenchymal transition via integrin α4-mediated signaling

Initiation-Confined Holographic Photopolymerization under Visible Light

Optimizing engine operating parameters for enhanced performance in a combustion-enhanced ternary-fuelled compression ignition engine

Abstract This research aims to determine an appropriate injection timing (IT) and exhaust gas recirculation rate (EGR) for optimal output factors on a compression ignition (CI) engine fuelled by diesel-mahua-ethanol blend combined with zinc oxide (ZnO) combustion enhancer using experimentation, response surface methodology (RSM) and artificial neural networks (ANN). The generated ANN and RSM models demonstrated enhanced prediction accuracy with high correlation coefficient (R2) values. The effects of IT and EGR rate were experimented at varying load conditions. The RSM established operating parameters for optimal output responses are 26.4° bTDC IT and 8.63% EGR rate for B25E15Zn50 blend. Finally, the process optimization by RSM has been validated with experimental results. The established engine operating parameters resulted in improvement of peak cylinder pressure (CP), heat release rate (HRR), brake thermal efficiency (BTE) by 12.3%, 9.9%, 3.7% respectively and also reduction in hydrocarbon (HC), carbon monoxide (CO), smoke, and nitrogen oxides (NOx) by 26.4%, 19.6%, 43.6% and 33.7% respectively at 80% load. This research signifies the benefit of RSM and ANN models for establishing engine operating parameters for optimal engine output responses.