P.M.N.M. Dental College and Hospital

The integration of machine learning (ML) techniques with biomedical implant systems has markedly transformed patient care, diagnostics, and therapy. MATLAB, esteemed for its robust computational capabilities and extensive toolboxes, has emerged as a preferred platform for the development and implementation of ML algorithms in biomedical applications. This review examines the current state of ML implementation using MATLAB across various biomedical implant applications, including neural prosthetics, cardiac implants, orthopedic devices, and continuous monitoring systems. We discuss key ML algorithms, such as support vector machines, neural networks, random forests, and deep learning architectures, which have been successfully implemented in MATLAB for signal processing, pattern recognition, and predictive analytics in implantable devices. This review also addresses challenges such as data quality, computational constraints, regulatory compliance, and real-time processing requirements. Future directions emphasize the need for edge computing integration, federated learning approaches, and enhanced interpretability of ML models in clinical settings. This comprehensive analysis provides researchers and practitioners with insights into leveraging MATLAB to advance intelligent biomedical implant technologies.

BackgroundSevere early childhood caries (s-ECC) remains a highly prevalent condition among children worldwide, posing a considerable burden in both developed and developing regions. This systematic review seeks to comprehensively identify, synthesize, and critically evaluate available evidence on salivary proteomic and peptidomic biomarkers that may aid in the diagnosis or risk assessment of ECC.Aim and objectivesThis study aims to evaluate any difference in the level of proteomic salivary biomarkers in children with and without ECC.MethodsA comprehensive search was conducted across four major electronic databases: the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE via PubMed, Scopus, and Google Scholar. From an initial yield of 1,253 records, 11 studies fulfilled the eligibility criteria and were included for data extraction. The risk of bias for the included studies was assessed using the QUADAS-2 tool for the Quality Assessment of Diagnostic Accuracy Studies.ResultsOf the 11 studies included, quality appraisal using the QUADAS-2 indicated that 3 out of 11 studies had low risk in all domains, while the remaining studies had unclear or high-risk domains.ConclusionThe pooled evidence suggests that salivary biomarkers have significant potential for detecting ECC. Utilizing salivary diagnostics may enhance the prevention of new carious lesions. Future clinical trials with robust methodologies are recommended to generate further conclusive evidence.Clinical significanceSalivary proteomic biomarkers, including MS protein, mucins, amylase, glycoproteins, proline, and glycine, may provide additional value in the early diagnosis of ECC in pediatric populations.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42023468566, PROSPERO CRD42023468566.

Endo-perio lesions present complex diagnostic and therapeutic challenges due to combined endodontic and periodontal pathology when managed in isolation. Therefore, it is of interest to evaluate multimodal treatment with laser curettage, crown rehabilitation and orthodontic therapy in 48 teeth from 42 patients. AI-assisted CBCT assessments showed 78.4±12.3% periapical lesion reduction, 45.6±8.9 HU bone density increase and 2.84±0.92 mm periodontal bone gain at 12 months. The integrated protocol achieved superior healing across compartments compared to traditional sequential approaches. AI-guided laser-prosthetic-orthodontic management advances endo-perio care by establishing a predictable, comprehensive standard with enhanced radiographic outcomes.

Roving survey was conducted to assess the incidence of onion twister disease in major growing districts of Karnataka during 2022-23 and 2023-24 covering seven districts in the first year and five districts in the second, with systematic field sampling and disease scoring using a 0-5 scale. Per cent Disease Index (PDI) was calculated and GPS coordinates of each field were recorded for spatial mapping. During 2022-23, mean PDI ranged from 18.28 per cent in Dharwad to 45.14 per cent in Chitradurga, with major hotspots identified in Chitradurga and Chikkamagaluru. In 2023-24, disease incidence declined markedly, with average PDI across districts reduced to 6.69 per cent, indicating improved crop management and favorable climatic conditions. Spatial distribution maps highlighted significant variability in disease occurrence across taluks. Results underscore the importance of continuous surveillance and adoption of integrated management strategies.

Human Metapneumovirus (HMPV) is a significant contributor to respiratory tract infections (RTI's), presenting a clinical spectrum that ranges from mild flu-like symptoms to severe pneumonia. The overlap of clinical features with other RTI's, often complicates its diagnosis. While HMPV predominately affects young children, the elderly and immunocompromised individuals, its true burden remains under-recognized due to diagnostic challenges. Currently there is no specific antiviral therapy or approved vaccine for HMPV, underscoring the urgent need for research into effective prevention and treatment strategies. Furthermore, the oral manifestations of HMPV infections remains an unexplored domain with potential correlations to viral kinetics and systemic spread. Investigating these oral signs could provide new insights into disease progression and serve as adjunctive diagnostic markers. The present paper provides a comprehensive review of the epidemiology, immunodynamics and diverse clinical manifestations of HMPV, while also shedding light on recent advancements in diagnostic approaches and vaccine development. By addressing these knowledge gaps, future research can pave the way for a more comprehensive approach to mitigate the global burden of HMPV-related respiratory illness.

The study investigated the use of LED light therapy in tooth extraction procedures, focusing on its effects on hemostasis and socket healing. LED therapy, typically used for composite resin polymerization, was explored for its potential to induce hemostasis through photo-thermo-coagulation. Fifty patients with bilateral extractions were selected. One socket received LED therapy (480nm wavelength, 20s), the other served as control. Bleeding cessation time and socket healing after 72h were evaluated. The LED-treated group showed significantly faster bleeding cessation (30.80 ± 5.22s) compared to the control group (161.04 ± 25.69s). After 72h, the LED-treated sockets exhibited better healing than the control group. The study suggested LED photocoagulation promotes rapid hemostasis and accelerates periodontal tissue healing. The mechanism involves vascular spasm, platelet aggregation, and coagulation. LED therapy shows promise as an alternative to traditional hemostatic methods, potentially reducing the need for sutures or local hemostatic agents. LED light therapy demonstrates significant potential in improving post-extraction hemostasis and healing, offering a novel approach in dental procedures and periodontal photo-engineering.