Journal of pharmacy & bioallied sciences最新文献

Background: To evaluate and compare salivary osteocalcin concentrations in individuals with dental implants, stratified according to smoking status.

Materials and methods: In total, 100 participants were recruited, comprising 50 smokers and 50 non-smokers. All the patients underwent dental implant therapy. Collection of salivary samples was done and analyzed for osteocalcin levels. The data obtained were systematically tabulated, and statistical analysis was performed using SPSS software.

Results: Group I (non-smokers) consisted of 50 individuals, while Group II (smokers) also comprised 50 individuals. The mean salivary osteocalcin concentration was 2.58 ng/mL in the non-smoker group and 1.69 ng/mL in the smoker group, indicating a substantial reduction among smokers.

Conclusion: Patients rehabilitated with dental implants who reported tobacco use exhibited diminished salivary osteocalcin concentrations when compared with their non-smoking counterparts, suggesting an adverse influence of smoking on peri-implant bone metabolic activity.

Problem considered: This investigation attempts to design and deploy a bespoke Random Forest model for early rate prediction of Type-II Diabetes utilizing the dataset available from NFHS-5.

Methods: Hyperparameter optimization was carried out to boost the performance of a Random Forest algorithm. Missing value treatments and class imbalance problems were done utilizing SMOTE. Accuracy, recall, log loss and ROC-AUC were utilized to test the model's performance.

Results: The accuracy, recall, log loss, and ROC-AUC score were 73.48%, 74.40%, 54.17%, and 81.17%, respectively. The study also indicates that demographic, lifestyle, comorbidities, and specific physiological characteristics significantly influence the prediction of diabetes.

Conclusion: Random Forest algorithms are extremely effective with early diabetes detection and could help support the design of focused intervention and policy measures.

Digital chrononutrition integrates wearable sensors, continuous glucose monitoring (CGM), and smart mobile applications to enable precision-based meal timing tailored to each individual's circadian and metabolic patterns. Wearables track biological rhythms, such as sleep cycles, heart rate variability, and body temperature to define optimal meal windows. CGMs capture detailed glucose profiles, highlighting how meals consumed at different times affect postprandial glycemic responses. When these data streams are combined via intelligent apps, users receive real-time, customized guidance on advancing or delaying meals to match peaks in insulin sensitivity. This has the potential to lead to improved glycemic control and metabolic stability compared to conventional static dietary advice. However, widespread adoption, especially in resource-limited settings, faces challenges stemming from high device costs, infrastructure gaps (electricity and internet), limited digital literacy, and insufficient local support. Overcoming these obstacles through subsidies, community health worker training, culturally tailored app design, and robust infrastructure can make precision chrononutrition a scalable and equitable solution in global diabetes care.

Background: Proper pain control before subarachnoid block (SAB) is must in proximal femur fractures as patients cannot tolerate movement due to severe pain. This study compares ultrasound-guided fascia iliaca compartment block (USG-FICB) with femoral nerve block (USG-FNB) for pre-SAB analgesia.

Materials and methods: Ninety patients scheduled for elective femur fracture surgery under SAB were randomly divided. Group FICB received 25 ml of 0.25% bupivacaine and Group FNB got 15 ml. Pain was assessed using Numerical Rating Scale (NRS) scale. Time for first rescue dose, side effects, and patient satisfaction also noted.

Results: Both groups had similar baseline NRS. Group FICB showed faster pain relief and better positioning comfort during SAB (NRS 3.00 vs. 4.28 P = 0.001). Time to first rescue analgesia was more in FICB (4.67 vs. 2.71 h P = 0.001). Satisfaction scores were also higher. Hemodynamics stayed stable, and adverse events were rare.

Conclusion: USG-FICB is more effective than USG-FNB in controlling pain before SAB in femur fracture surgery.

Neglected Tropical Diseases (NTDs) affect over a billion people globally, primarily in low- and middle-income countries. These diseases contribute significantly to morbidity, disability, and poverty, yet receive inadequate attention in global health policies and funding frameworks. A systematic literature search was conducted in PubMed, Scopus, and Web of Science for studies published between 2018 and 2024. Inclusion criteria comprised original research, policy reviews, and global reports on WHO-recognized NTDs. A total of 15 articles were included, selected based on methodological rigor and relevance. Data were synthesized into thematic categories, including burden, interventions, and policy. Studies revealed significant geographical and health disparities, with disruptions due to COVID-19, underfunding, and poor integration into primary care. Effective strategies included WASH integration, translational technologies, and policy alignment with Universal Health Coverage. Research gaps persist in pediatric populations and community-level participation. Addressing NTDs requires resilient, multisectoral approaches embedded within primary healthcare systems. Global efforts must prioritize equity, local adaptation, and sustainable financing to meet 2030 elimination targets.

Introduction: QMix is a newer formulation known to be a more effective irrigant. A polypropylene canal brush facilitates the introduction of surfactant in the canal.

Objective: To assess the efficacy of QMix with canal brush to eliminate smear layer and debris in a root canal that is instrumented with ProTaper Next and WaveOne files under scanning electron microscope.

Methods: Forty single-rooted premolars were allocated into four groups:1) ProTaper Next file, 2) ProTaper Next file + Rotary Canal Brush, 3) WaveOne file, 4) WaveOne file + Rotary Canal Brush. All files were used up to size 40 in their respective tapering. For irrigation, sodium hypochlorite (5.25%) was used throughout instrumentation and QMix was used after instrumentation. The rotary canal brush in assigned groups was used for 30 sec along with QMix, followed by irrigation with 5ml distilled water. The roots were sectioned into two halves and analyzed under SEM.

Results: The mean scores for debris and smear layer were lesser in WaveOne and WaveOne with canal brush groups compared to other groups.

Conclusion: The use of canal brush created cleaner canal walls than without canal brush. The most superior results were delivered by WaveOne rotary file.

Background: Artificial Intelligence (AI) has gained significant traction in health care, particularly in automating documentation processes. AI-generated emergency department (ED) summaries have the potential to enhance hospital handoff efficiency and improve patient outcomes. However, their accuracy, reliability, and impact on clinical workflow remain underexplored. This study evaluates AI-generated ED summaries in comparison with traditional physician-written reports concerning handoff efficiency, documentation time, and patient safety.

Materials and methods: A prospective, randomized controlled study was conducted in a tertiary care hospital over six months. A total of 500 ED patients were included, randomly assigned into two groups: AI-generated summaries (n = 250) and physician-written summaries (n = 250). The AI model utilized for summary generation was trained using natural language processing (NLP) techniques. Key evaluation parameters included handoff completion time, documentation accuracy, physician satisfaction (measured on a 5-point Likert scale), and adverse patient events. Statistical analysis was performed using SPSS v. 26, with significance set at P < 0.05.

Results: The AI-generated summaries significantly reduced handoff completion time (8.2 ± 1.5 min vs. 12.7 ± 2.1 min, P < 0.001). Documentation accuracy was comparable between AI-generated and traditional reports (92.4% vs. 94.1%, P = 0.07). Physician satisfaction was higher for AI-generated summaries (4.3 ± 0.6 vs. 3.7 ± 0.9, P < 0.01). Adverse events were not significantly different between the groups (2.4% vs. 2.8%, P = 0.62).

Conclusion: AI-generated ED summaries demonstrate significant potential in enhancing hospital handoff efficiency by reducing completion time without compromising accuracy or patient safety. The findings suggest that AI integration into clinical documentation could improve workflow efficiency and physician satisfaction. Further large-scale studies are required to validate these results across different healthcare settings.

Introduction: This randomized controlled clinical trial compared the radiographic periapical healing outcomes of single-visit versus multiple-visit root canal treatments for teeth with apical periodontitis.

Materials and methods: Thirty patients with single-rooted teeth diagnosed with primary apical periodontitis were randomly allocated to either a single-visit (n = 15) or a multiple-visit (n = 15) group. Periapical healing was radiographically evaluated using the Periapical Index (PAI) score at baseline and at 6 and 12-month follow-up appointments.

Results: The results showed no statistically significant difference in the radiographic healing rates between the two groups at either follow-up period. The success rates (PAI score ≤2) were 93.3% for the single-visit group and 86.7% for the multiple-visit group at 12 months (P > 0.05).

Conclusion: These findings suggest that both treatment protocols are equally effective in achieving periapical healing, challenging the necessity of an inter-appointment intracanal medicament.

Background: Surgical site infections (SSIs) are among the most common healthcare-associated infections, contributing to significant morbidity and prolonged hospital stays. This study aimed to determine the prevalence and risk factors associated with early SSIs in patients undergoing elective neurosurgery and laparotomy at a tertiary care center in central India.

Material and methods: This prospective observational study was conducted over one year, including 721 patients who underwent elective neurosurgical or laparotomy procedures. Data on demographics, comorbidities, type of surgery, antibiotic prophylaxis timing, and surgical duration were collected. SSIs were identified using CDC/NHSN criteria, and microbiological evaluation was performed for infected cases.

Results: The overall prevalence of SSIs was 15.53%, with higher incidence among patients aged 21-60 years and males. Diabetes mellitus was the most common comorbidity in SSI patients. SSIs were more frequent in laparotomy cases compared to neurosurgery. Notably, over half of the infected patients had received prophylactic antibiotics within 30 minutes before incision.

Conclusion: SSIs remain a substantial postoperative complication, particularly in diabetic and laparotomy patients. Adherence to comprehensive infection control practices beyond antibiotic timing is crucial to minimize infection risk.

Objective: To evaluate the antimicrobial efficacy and physical properties of antibiotic-modified GIC, propolis-incorporated GIC, and conventional GIC.

Study design: An antibiotic mixture containing ciprofloxacin, metronidazole, and doxycycline and propolis tincture were mixed with GIC to obtain two study groups and Fuji IX as a control group. Antibacterial action, fluoride release, and physical properties were evaluated.

Results: Addition of antibiotics and propolis to GIC increased its antimicrobial action without negatively modifying physical properties of conventional GIC.

Conclusion: Hence, the incorporation of antibiotics and propolis into GIC provided an acceptable combination of properties.