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Risedronate-loaded mPEG-coated hydroxyapatite, thiolated chitosan-based (coated) and non-coated nanoparticles were tested for their potential effects in the treatment of osteoporosis. The prepared nanoparticles were evaluated for their bone-targeting potential by inducing osteoporosis in female Wistar rats via oral administration of Dexona (dexamethasone sodium phosphate). In vivo pharmacokinetic and pharmacodynamic studies were performed on osteoporotic rat models treated with different formulations. The osteoporotic model treated with the prepared nanoparticles indicated a significant effect on bone. The relative bioavailability was enhanced for RIS-HA-TCS-mPEG nanoparticles given orally compared to RIS-HA-TCS, marketed, and API suspension. Biochemical investigations also showed a significant change in biomarker levels, ultimately leading to bone formation/resorption. Micro-CT analysis of bone samples also demonstrated that the RIS-HA-TCS-mPEG-treated group showed the best results compared to other treatment groups. Moreover, the histology of bone treated with RIS-HA-TCS-mPEG showed a marked restoration of the architecture of trabecular bone along with a well-connected bone matrix and narrow inter-trabecular spaces compared to the toxic group. A stability analysis was also carried out according to ICH guidelines (Q1AR2), and it was found that RIS-HA-TCS-mPEG was more stable than RIS-HA-TCS at 25 °C. Thus, the results of present study indicated that mPEG-RIS-HA-TCS has excellent potential for sustained delivery of RIS for the treatment and prevention of osteoporosis, and for minimizing the adverse effects of RIS typically induced via oral administration.

Machine learning (ML) algorithms have brought about a revolution in many industries where otherwise operation time, cost, and safety would have been compromised. Likewise, in lubrication research, ML has been utilized on many occasions. This review provides an in-depth understanding of seven ML algorithms from a tribological perspective. More specifically, it presents a comprehensive overview of recent advancements in ML applied to lubrication research, organized into four distinct categories. The first category, experimental parameter prediction, highlights the significant contributions of artificial neural networks (ANNs) in accurately forecasting operating conditions related to friction and wear. These predictions offer valuable insights that aid in forensic preparation. Discriminant analysis, Bayesian modeling, and transfer learning approaches have also been used to predict experimental parameters. Second, to predict the lubrication film thickness and identify the lubrication regime, algorithms such as logistic regression and ANN were useful. Such predictions provide up to 99.25% accuracy. Third, to predict the friction and wear for a given experimental condition, support vector machine (SVM), polynomial regression, and ANN offered an accuracy above 93%. Finally, for condition monitoring for bearings, gearboxes, gear trains, and similar critical situations where regular in-person inspection is difficult, Naïve Bayes, SVM, decision trees, and ANN were utilized to predict the safe life of lubricants. This review highlighted these four aspects with state-of-the-art examples and discussed the current situation and projected future possibilities of lubricant design facilitated by ML techniques.

Vahid Asgary,1,2 Alireza Shoari,1 Fahimeh Baghbani-Arani,3 Seyed Ataollah Sadat Shandiz,4 Mohammad Sadeq Khosravy,5 Alireza Janani,1 Razieh Bigdeli,6 Rouzbeh Bashar,1 Reza Ahangari Cohan1,7 1Virology Research Group, Department of Rabies, Pasteur Institute of Iran, 2Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, 3Department of Genetics and Biotechnology, School of Biological Science, Varamin-Pishva Branch, Islamic Azad University, Varamin, 4Young Researchers and Elite Club, East Tehran Branch, Islamic Azad University, 5Department of Laboratory of Animal Sciences, Pasteur Institute of Iran, 6Department of Genetic, Science and Research Branch, Islamic Azad University, 7New Technologies Research Group, Department of Pilot Nanobiotechnology, Pasteur Institute of Iran, Tehran, Iran Background: Green synthesis of nanoparticles by plant extracts plays a significant role in different applications. Recently, several studies were conducted on the use of nanoparticles as adjuvant. The main aim of this study was to evaluate green synthesized silver nanoparticles (AgNPs) as adjuvant in rabies veterinary vaccine and compare the results with the existing commercially available alum adjuvant.Materials and methods: In the current study, AgNPs were prepared by the reduction of aqueous silver nitrate by leaf extract of Eucalyptus procera. The formation of AgNPs was confirmed by ultraviolet (UV)–visible spectrophotometer, scanning electron microscopy, dynamic light scattering, and X-ray diffraction analysis. Then, different amounts of AgNPs (200 µg, 400 µg, 600 µg, and 800 µg) were added to 1 mL of inactivated rabies virus. The loaded vaccines (0.5 mL) were injected intraperitoneally into six Naval Medical Research Institute mice in each group on days 1 and 7. On the 15th day, the mice were intracerebrally challenged with 0.03 mL of challenge rabies virus (challenge virus strain-11, 20 lethal dose [20 LD50]), and after the latency period of rabies disease in mice (5 days), the mice were monitored for 21 days. Neutralizing antibodies against rabies virus were also investigated using the rapid fluorescent focus inhibition test method. The National Institutes of Health test was performed to determine the potency of optimum concentration of AgNPs as adjuvant. In vitro toxicity of AgNPs was assessed in L929 cell line using MTT assay. In addition, in vivo toxicity of AgNPs and AgNPs-loaded vaccine was investigated according to the European Pharmacopeia 8.0.Results: AgNPs were successfully synthesized, and the identity was confirmed by UV–visible spectrophotometry and X-ray diffraction analysis. The prepared AgNPs were spherical in shape, with an average size of 60 nm and a negative zeta potential of -14 mV as determined by dynamic light scattering technique. The highest percentage of viability was observed at 15 mg/kg and 20 mg/kg of AgNPs-loaded vaccine concentrations after injecting into the mice. The calculated potencies for alum-containing vaccine and AgNPs-loaded vaccine (dose 15 mg/kg) were 1.897 and 1.303, respectively. MTT assay demonstrated that alum at the concentration of 10 mg/mL was toxic, but AgNPs were not toxic. The in vivo toxicity also elucidated the safety of AgNPs and AgNPs-loaded vaccine in mice and dogs, respectively.Conclusion: In the current study, for the first time, the adjuvanticity effect of green synthesized AgNPs on veterinary rabies vaccine potency with no in vivo toxicity was elucidated according to the European Pharmacopeia 8.0. Keywords: green synthesis, nanoparticles, rabies virus, adjuvant

Statement of Problem: Adhesion to root canal dentin is one of the necessary characteristics of endodontic sealers. MTA Fillapex (Angelus, Londria, PR, Brazil) is one of the recently introduced MTA-based sealer. Only a limited number of studies have evaluated the bonding of this sealer to the dentin, with conflicting results. Objectives: The purpose of this study was to evaluate the bond strength of MTA Fillapex sealer) to root canal dentin compared to an epoxy resinbased sealer (AH26 sealer). Materials and Methods: Twenty root blocks (6mm length) were acquired from the middle part of the extracted single-rooted central or lateral maxillary incisor roots and their lumens were enlarged to achieve a diameter of 1.3 mm. MTA Fillapex and AH26 were placed in the lumens of root blocks (n = 10). The specimens were kept in an incubator for 7 days, the roots were sectioned perpendicular to their long axis to achieve slices with thickness of 1 mm and the push-out test was carried out by a universal testing machine. Results: The mean value of bond strength was 1.84 ± 0.7 Mpa for AH26 and 0.19 ± 0.1 Mpa for MTA Fillapex. The difference between the two groups was statistically significant (p < 0.001). Conclusions: With in the limitation of this study, AH 26 showed significantly higher bond strength in comparison to MTA Fillapex.

Statement of Problem: The pH of the human abscess has been measured as low as 5.0. This low pH could potentially inhibit setting reactions, affect adhesion, or increase the solubility of root end filling materials hence affect the compressive strength. Moreover, root end filling materials might expose or even mix with lidocaine HCL during periapical surgery. Objectives: The aim of this in vitro study was to evaluate the effect of acidic pH and lidocaine on the compressive strength of calcium-enriched mixture (CEM). Materials and Methods: CEM was mixed according to the manufacturer’s instructions or with lidocaine (L), and condensed into 6 × 4 mm split moulds. The samples were exposed to phosphate buffered saline (PBS) at pH 5 or 7.4 for 7 or 28 days. Cylindrical blocks of CEM (total number = 120 and 15 for each group) were subjected to compressive strength test using a universal testing machine. Data were analysed using three-factor analysis of variance (ANOVA). Results: Regardless of pH and time, significant differences were not found between lidocaine groups and the groups that were mixed according to the manufacturer’s instruction (p = 0.083). For both mixing agents, regardless of time, there were no significant differences between the two pH levels (p = 0.157). Regardless of the material and pH, there was a significant increase in the compressive strength from days 7 to 28 (p < 0.001). Conclusions: Mixtures with lidocaine and exposure to an acidic environment had no adverse effects on the compressive strength of CEM Cement.

Statement of Problem: Calcium hydroxide which is commonly used as an intracanal medicament, changes the pH of dentin and periradicular tissues to an alkaline pH. In some clinical situations, endodontic reparative cements like calcium enriched mixture cement are used after calcium hydroxide therapy. However, the alkaline pH may affect the physical properties of this cement. Objectives: This study was designed to evaluate the effect of alkaline pH on the push-out bond strength of calcium enriched mixture. Materials and Methods: 80 root slices were prepared from single-rooted human teeth and their lumens were instrumented to achieve a diameter of 1.3mm. Calcium enriched mixture (CEM) was mixed according to the manufacturer’s instruction and introduced into the lumens of root slices. The specimens were then randomly divided into 4 groups (n = 20) and wrapped in pieces of gauze soaked in synthetic tissue fluid (STF) buffered in potassium hydroxide at pH values of 7.4, 8.4, 9.4, or 10.4. The samples were incubated for 4 days at 37°C. The push-out bond strengths were then measured using a universal testing machine. Failure modes were examined under a light microscope at ×20 magnification. The data were analyzed using one-way analysis of variance and Tukey’s post hoc tests. Results: The greatest (1.41 ± 0.193 MPa) and lowest (0.8 ± 0.06 MPa) mean push-out bond strengths were observed after exposure to pH values of 7.4 and 8.4, respectively. There were significant differences between the neutral group and the groups with pH of 8.4 (p = 0.008) and 10.4 (p = 0.022). The bond failure was predominantly of cohesive type for all experimental groups. Conclusions: Under the condition of this study, alkaline pH adversely affected the Push-out bond strength of CEM cement.

Purpose: Human papillomavirus (HPV)-related head and neck squamous cell carcinoma (HNSCC) is associated with daily marijuana use and is also increasing in parallel with increased marijuana use in the United States. Our study is designed to define the interaction between cannabinoids and HPV-positive HNSCC., Experimental Design: The expression of cannabinoid receptors CNR1 and CNR2 was analyzed using The Cancer Genome Atlas (TCGA) HNSCC data. We used agonists, antagonists, siRNAs, or shRNA-based models to explore the roles of CNR1 and CNR2 in HPV-positive HNSCC cell lines and animal models. Cannabinoid downstream pathways involved were determined by Western blotting and analyzed in a primary HPV HNSCC cohort with single-sample gene set enrichment analysis (ssGSEA) and the OncoGenome Positioning System (Onco-GPS)., Results: In TCGA cohort, the expression of CNR1 and CNR2 was elevated in HPV-positive HNSCC compared with HPV-negative HNSCC, and knockdown of CNR1 / CNR2 expression inhibited proliferation in HPV-positive HNSCC cell lines. Specific CNR1 and CNR2 activation as well as nonselective cannabinoid receptor activation in cell lines and animal models promoted cell growth, migration, and inhibited apoptosis through p38 MAPK pathway activation. CNR1/CNR2 antagonists suppressed cell proliferation and migration and induced apoptosis. Using whole-genome expression analysis in a primary HPV HNSCC cohort, we identified specific p38 MAPK pathway activation signature in tumors from HPV HNSCC patients with objective measurement of concurrent cannabinoid exposure., Conclusions: Cannabinoids can promote progression of HPV-positive HNSCC through p38 MAPK pathway activation., (©2020 American Association for Cancer Research.)

Background: Since vitamin D is a recent known immunoregulatory factor in some diseases which are addressed in immune system disorders such as SLE, [psoriasis] and others., Objective: To determine the serum levels of 25-hydroxy vitamin D3 [25(OH)D] in patients with acne vulgaris and its association with clinical features., Methods: This cross-sectional study was conducted over months. This study included 39 patients with acne vulgaris and 40 healthy controls. Subjects who did not use alcohol, vitamin D supplements, oral steroids or PUVA and/or NBUVB for more than three months were included. Serum 25(OH)D levels were measured. Baseline demographics, family history and comorbidities like PCO were recorded. Statistical analyses were performed using SPSS 16.0.0., Results: The median concentration of 25(OH)D was 8.4 ng/mL (range: 1.4-99) in patients and 10.4 ng/mL (range: 3.1-56.7) in controls, with no statistical significant difference. PCOS was a significant predictor of the occurrence of acne vulgaris (OR=6.25; 95% CI: 1.52-25.66; p=0.01). There were no significant associations between severity of disease and serum 25(OH)D levels (rs=-0.12, p=0.45), age (rs=-0.28, p=0.09), BMI (rs=-0.12, p=0.46), age at onset of disease (rs=-0.08, p=0.63) and duration of disease (rs=-0.10, p=0.54)., Conclusion: Based on the previous studies this is highly suspected that vitamin D would be a prominent factor in acne patients and more performances with bigger sample size could be useful to get positive results.

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins.

Due to the increasing gap between structure-determined and sequenced proteins, prediction of protein structural classes has been an important problem. It is very important to use efficient sequential parameters for developing class predictors because of the close sequence-structure relationship. The multinomial logistic regression model was used for the first time to evaluate the contribution of sequence parameters in determining the protein structural class. An in-house program generated parameters including single amino acid and all dipeptide composition frequencies. Then, the most effective parameters were selected by a multinomial logistic regression. Selected variables in the multinomial logistic model were Valine among single amino acid composition frequencies and Ala-Gly, Cys-Arg, Asp-Cys, Glu-Tyr, Gly-Glu, His-Tyr, Lys-Lys, Leu-Asp, Leu-Arg, Pro-Cys, Gln-Met, Gln-Thr, Ser-Trp, Val-Asn and Trp-Asn among dipeptide composition frequencies. Also a neural network model was constructed and fed by the parameters selected by multinomial logistic regression to build a hybrid predictor. In this study, self-consistency and jackknife tests on a database constructed by Zhou [1998. An intriguing controversy over protein structural class prediction. J. Protein Chem. 17(8), 729-738] containing 498 proteins are used to verify the performance of this hybrid method, and are compared with some of prior works. The results showed that our two-stage hybrid model approach is very promising and may play a complementary role to the existing powerful approaches.