Effectiveness of the 'Mesiodistal Guide Set' in Dental Implant Placement: A Clinical Trial

Objectives This study used a newly designed mesiodistal guide set to assess and compare the precision of the mesiodistal positioning of dental implants relative to adjacent teeth. The cost-effective and convenient guide set was compared to the conventional freehand surgery technique in partially edentulous patients. Methods The study enrolled 38 patients requiring at least one implant. Participants were divided into case and control groups, receiving 30 implants in each group: 10 in free-end areas, and 20 in single-tooth edentulous spaces. In the case group, implants were placed using the mesiodistal guide set, while the control group underwent the freehand procedure. The postoperative evaluation involved taking parallel periapical radiographs to measure distances and angles between the implants and adjacent teeth using Photoshop CS4. The data was rigorously analyzed using the Generalized Estimating Equations (GEE) method, a statistical technique for modeling clustered data, with statistical significance set at p<0.05. Results Measurements of the mesiodistal distances between implants and adjacent teeth showed reduced deviations in the case group, with statistically significant differences in mesial (P=0.001) and distal (P=0.036) distances. The tooth-supported area exhibited better outcomes compared to the free-end area. However, there were no significant differences in implant-tooth angulation, whether mesial (P=0.503) or distal (P=0.188). Conclusion The study indicated that the mesiodistal guide set offers practical guidance for positioning implants next to teeth in partially edentulous patients. This finding has significant practical implications, providing tangible evidence for the clinical application of the guide set. Despite some limitations, the findings fall within clinically acceptable parameters, and the guide set proved to enhance accuracy over the freehand method

Voice Handicap Index (VHI) in Persian Speaking Parkinson\'s Disease Patients

Objectives: “Voice” is affected more and sooner than other speech subsystems in Parkinson's Disease (PD). Voice Handicap Index (VHI) is the most applicable subjective self-rating questionnaire in VD patients. The aim of this study was the investigation of Voice handicap in Iranian PD patients. Methods & Materials: This cross-sectional, analytical and non-interventional study was done on 50 (35 males, 15 females) patients who reported a VD related to their PD. They were selected from thepatients referring to movement disorders’ clinic in Rasool Akram Hospital affiliated withTehran University of medical sciences, through easy sampling. VHI total score (VHIT) and its domains (functional-VHIF, Emotional VHIE, Physical VHIP) was assessed in all of participants and by gender segregation. Results: 83% of patients reported voice handicap. There wasn't any difference between VHIT and its mentioned 3 domains in both sexes. There is positive correlation between VHIT, VHIE and VHIF with age. VHIT and VHIF had a positive relationship with disease duration (DD). The males VHIT and the mentioned domains had positive correlations with DD. Conclusion: Most of Iranian PD patients feel handicap due to voice disorder caused by PD and their quality of life was affected by voice impairment. Increase in age and disease duration caused more voice disorder and reduced quality of life especially patients feel more handicaps in functional domain (VHIF). In addition, the males feel more handicap than females when DD develops

Graphene-Based Smart Insole Sensor for Pedobarometry and Gait Analysis

Pedobarography and gait analysis are crucial components of healthcare, given their close association with chronic illnesses. Wearable electronics, such as smart insole systems, have emerged as a promising technology for remote and long-term gait monitoring, providing more accurate data in real time. To meet the demands of wearable electronics, components with both flexibility and sensitivity are crucial. This letter employed low-cost and rapid prototyping techniques to develop a flexible piezoresistive pressure sensor with a graphene nanoplatelet/polycaprolactone composite spray-deposited on a fiber-based commercial insole. The sensor demonstrated a pressure range of up to 400 kPa and high linear sensitivity (similar to 0.376 kPa(-1)), making it suitable for pedobarography. In addition, to test its practical application, the sensor was linked to a low-power programmable data logging and transmitting device for data acquisition over custom designed android application. The sensor can differentiate various motion states, such as walking, jumping, and running, while also demonstrating potential for long-term stability

Wearable Graphene-based smart face mask for Real-Time human respiration monitoring

After the pandemic of SARS-CoV-2, the use of face-masks is considered the most effective way to prevent the spread of virus-containing respiratory fluid. As the virus targets the lungs directly, causing shortness of breath, continuous respiratory monitoring is crucial for evaluating health status. Therefore, the need for a smart face mask (SFM) capable of wirelessly monitoring human respiration in real-time has gained enormous attention. However, some challenges in developing these devices should be solved to make practical use of them possible. One key issue is to design a wearable SFM that is biocompatible and has fast responsivity for non-invasive and real-time tracking of respiration signals. Herein, we present a cost-effective and straightforward solution to produce innovative SFMs by depositing graphene-based coatings over commercial surgical masks. In particular, graphene nanoplatelets (GNPs) are integrated into a polycaprolactone (PCL) polymeric matrix. The resulting SFMs are characterized morphologically, and their electrical, electromechanical, and sensing properties are fully assessed. The proposed SFM exhibits remarkable durability (greater than1000 cycles) and excellent fast response time (∼42 ms), providing simultaneously normal and abnormal breath signals with clear differentiation. Finally, a developed mobile application monitors the mask wearer's breathing pattern wirelessly and provides alerts without compromising user-friendliness and comfort