Hybrid Predictive Machine Learning Model for the Prediction of Immunodominant Peptides of Respiratory Syncytial Virus.

Respiratory syncytial virus (RSV) is a common respiratory pathogen that infects the human lungs and respiratory tract, often causing symptoms similar to the common cold. Vaccination is the most effective strategy for managing viral outbreaks. Currently, extensive efforts are focused on developing a vaccine for RSV. Traditional vaccine design typically involves using an attenuated form of the pathogen to elicit an immune response. In contrast, peptide-based vaccines (PBVs) aim to identify and chemically synthesize specific immunodominant peptides (IPs), known as T-cell epitopes (TCEs), to induce a targeted immune response. Despite their potential for enhancing vaccine safety and immunogenicity, PBVs have received comparatively less attention. Identifying IPs for PBV design through conventional wet-lab experiments is challenging, costly, and time-consuming. Machine learning (ML) techniques offer a promising alternative, accurately predicting TCEs and significantly reducing the time and cost of vaccine development. This study proposes the development and evaluation of eight hybrid ML predictive models created through the permutations and combinations of two classification methods, two feature weighting techniques, and two feature selection algorithms, all aimed at predicting the TCEs of RSV. The models were trained using the experimentally determined TCEs and non-TCE sequences acquired from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) repository. The hybrid model composed of the XGBoost (XGB) classifier, chi-squared (ChST) weighting technique, and backward search (BST) as the optimal feature selection algorithm (ChST-BST-XGB) was identified as the best model, achieving an accuracy, sensitivity, specificity, F1 score, AUC, precision, and MCC of 97.10%, 0.98, 0.97, 0.98, 0.99, 0.99, and 0.96, respectively. Additionally, K-fold cross-validation (KFCV) was performed to ensure the model's reliability and an average accuracy of 97.21% was recorded for the ChST-BST-XGB model. The results indicate that the hybrid XGBoost model consistently outperforms other hybrid approaches. The epitopes predicted by the proposed model may serve as promising vaccine candidates for RSV, subject to in vitro and in vivo scientific assessments. This model can assist the scientific community in expediting the screening of active TCE candidates for RSV, ultimately saving time and resources in vaccine development.

Effect of chloroquine pre-treatment on the metoclopramide's pharmacokinetics after their co-administration.

BACKGROUND: This study evaluated the pharmacokinetic interactions of orally administered chloroquine and metoclopramide. METHODS: The study employed a randomized and two-phase cross-over design with 4-week washout plan. Twelve healthy male volunteers were shortlisted according to the set criteria and were administered with metoclopramide 10 mg PO and chloroquine (a total of 1500 mg) at different intervals which were (500 mg at 0, 6, and 24 h). The concentration of chloroquine and metoclopramide in the blood samples was estimated using a validated HPLC-UV technique to affirm the maximum concentration (Cmax), time to reach Cmax (Tmax), and area under the curve (AUC). RESULTS: Cmax, T1/2, and AUC of metoclopramide were increased up to 20, 10, and 47.8%, respectively, by the concomitantly administering Chloroquine. Chloroquine-treated phase showed increased values of Cmax (ng/ml), AUC (ng.h/ml), and T½ (h), i.e. 41.35 ± 1.61, 504.12 ± 66.25, and 5.72 ± 2.63, as compared to that reference phase i.e. 34.52 ± 4.92, 341.14 ± 112.8, and 5.19 ± 1.14, respectively. CONCLUSIONS: Chloroquine was found to attenuate CYP2D6 activity in healthy Pakistani male volunteers. Hence, patients that are prescribed with metoclopramide or other CYP2D6-substrate drugs require a dose adjustment when administered with chloroquine.

SPE-UHPLC-ESI-MS/MS Analysis of Cocaine and Its Metabolites in Conventional and Alternative Biological Specimens: Application to Real Samples.

An increase in cocaine abuse has been observed globally since the past decade. Cocaine is among the commonly abused stimulants used for recreational purposes. In this study, the SPE-UHPLC-MS/MS method was developed and validated to be applied on real specimens of 20 chronic cocaine abusers to quantify cocaine/metabolites in conventional as well as alternative biological matrices. Cocaine was extracted from biological specimens using solid-phase extraction followed by liquid chromatography tandem mass spectrometry analysis. Chromatographic separation was achieved on a Poroshell120EC-18 column (2.1 mm × 50 mm, 2.7 µm particle size) using water-acetonitrile in 0.1% formic acid as a mobile phase in gradient elution mode. The flow rate of the mobile phase was 0.5 mL/min with a gradient varying the percentage of acetonitrile linearity ranging 15-95% in 6.0 min acquisition time, and the injection volume was set at 5 µL. Positive electrospray ionization with multireaction ion monitoring mode using two ion transitions for cocaine/metabolites and one for cocaine-d3 was employed. The quantification method demonstrated good linear ranges of 0.025-250 ng/mL in blood, urine, and oral fluid (ng/mg for hair and nail) with a ≥0.991% determination coefficient. The detection limit and lower quantification limit were 0.005 and 0.025 ng/mL in all matrices, respectively. The mean extraction recovery and ionization suppression ranged from 89.3 to 99.8% and -4.6 to -14.4% in the studied matrices. Within-run and between-days precisions were 1.8-7.2% and 1.9-6.1%, respectively. This study will not only help in quantifying cocaine/metabolites in alternative specimens (hair, nail, and oral fluid) but also guide clinical and forensic toxicologists in interpretation of exhumation cases. Furthermore, multiple specimens' analyses can be of significance in estimating the time/manner of drug exposure, in confirming the results of laboratories in cases of doubtful clinical histories, or in aiding medico-legal investigations.

Physicochemical properties-based hybrid machine learning technique for the prediction of SARS-CoV-2 T-cell epitopes as vaccine targets.

Majority of the existing SARS-CoV-2 vaccines work by presenting the whole pathogen in the attenuated form to immune system to invoke an immune response. On the other hand, the concept of a peptide based vaccine (PBV) is based on the identification and chemical synthesis of only immunodominant peptides known as T-cell epitopes (TCEs) to induce a specific immune response against a particular pathogen. However PBVs have received less attention despite holding huge untapped potential for boosting vaccine safety and immunogenicity. To identify these TCEs for designing PBV, wet-lab experiments are difficult, expensive, and time-consuming. Machine learning (ML) techniques can accurately predict TCEs, saving time and cost for speedy vaccine development. This work proposes novel hybrid ML techniques based on the physicochemical properties of peptides to predict SARS-CoV-2 TCEs. The proposed hybrid ML technique was evaluated using various ML model evaluation metrics and demonstrated promising results. The hybrid technique of decision tree classifier with chi-squared feature weighting technique and forward search optimal feature searching algorithm has been identified as the best model with an accuracy of 98.19%. Furthermore, K-fold cross-validation (KFCV) was performed to ensure that the model is reliable and the results indicate that the hybrid random forest model performs consistently well in terms of accuracy with respect to other hybrid approaches. The predicted TCEs are highly likely to serve as promising vaccine targets, subject to evaluations both in-vivo and in-vitro. This development could potentially save countless lives globally, prevent future epidemic-scale outbreaks, and reduce the risk of mutation escape.

ABO and Rhesus blood group markers as predictors in colorectal cancer: A prospective observational study.

Numerous research studies have investigated the relationship between ABO and Rhesus (Rh) blood groups and the risk of various cancers, yielding diverse findings. While these blood groups have been established as prognostic factors in some cancers, their relevance to colorectal cancer (CRC) remains uncertain. This research aims to determine the link between CRC and the ABO and Rh blood groups and explore any potential implications for disease survival. A hospital-based prospective observational study was conducted from March 2019 to March 2022 at the Sher-I-Kashmir Institute of Medical Sciences in Srinagar, India. A total of 246 patients with confirmed colorectal cancer were enrolled in the study. Our study observed that blood type B (33.74%) and Rh-positive (91.87%) blood types were the most prevalent, surpassing other blood groups. No statistically significant associations were identified between the blood groups and the studied xenobiotic-metabolizing enzyme gene variants. The study observed a heightened risk of CRC in patients with advanced cancer stages and lymphovascular invasion (P-value < .05). On follow-up, there were no statistically significant differences in 3-year survival rates observed between ABO and Rh blood groups. This study's findings suggest that ABO and Rh blood groups are not associated with the risk of CRC or overall survival among CRC patients. Further clinical studies are needed to establish the precise relationship between blood groups and CRC risks, as well as their implications for the prognosis of CRC patients.

Spilanthes acmella Extract-Based Natural Oils Loaded Emulgel for Anti-Microbial Action against Dermatitis.

BACKGROUND: Dermatitis is skin disorder that is complicated by recurrent infections of skin by bacteria, viruses, and fungi. Spilanthol is an active constituent of Spilanthes acmella, which possess strong anti-bacterial properties. The purpose of this study was to develop a herbal emulgel for the treatment of dermal bacterial infections, as microscopic organisms have created solid resistance against anti-microbials. METHODS: Emulgels were prepared and characterized for parameters such as physical examination, rheological studies, spreading coefficient, bio-adhesive strength measurement, extrudability study, antibacterial activity, FTIR analysis, in vitro drug dissolution, and ex vivo permeation studies. RESULT: With a statistically significant p-value = 0.024, 100% antibacterial activity was observed by F4 against Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli (mean ± S.D) (25.33 ± 0.28, 27.33 ± 0.5, and 27 ± 0.5). However, maximum antibacterial effect 100% formulations produced zones of inhibitions against E. colip-value = 0.001. The mean zone of inhibition produced by F4 was greatest among all at 26.44 ± 0.37 mm (mean ± S.D). The F4 formulation produced a maximum percentage dissolution, permeation, and flux of 86.35 ± 0.576, 55.29 ± 0.127%, and 0.5532 ug/cm2/min, respectively. CONCLUSIONS: The present study therefore, suggests the use of S. acmella extract and olive oil containing emulgel for treating bacterial skin infections.

Nosocomial SARS-COVID-19 Outbreak During the Third Wave of COVID-19 in an Oncology Facility at a Tertiary Care Hospital in Kashmir, India.

BACKGROUND: Coronavirus disease 2019 (COVID-19) emerged as a life-threatening respiratory condition, especially in immunocompromised patients, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Initially detected in China in December 2019, the first case in India was diagnosed on January 30, 2020. Here we report a nosocomial COVID-19 outbreak among cancer patients and healthcare workers (HCWs) in a medical oncology unit of a tertiary care hospital from our region. MATERIALS AND METHODS: This was a descriptive study of the nosocomial COVID-19 outbreak and was conducted in the month of January 2022 at the medical oncology unit of a tertiary care hospital in Srinagar, Jammu and Kashmir (J&K), India. The study included 25 COVID-19 cases, including patients and HC/non-HCWs (NHCWs). The confirmation of diagnosis was done through real-time polymerase chain reaction (RT-PCR) using nasopharyngeal/oropharyngeal swabs as the test sample. RESULTS: Twenty-five COVID-19 cases, including 14 admitted patients, nine HCWs, and two NHCWs were confirmed by COVID-19 RT-PCR in a span of 11 days. The first case was a positive HCW. The patients were admitted for management of various hematological as well as solid organ malignancies. Of the 14 patients, eight were in the pediatric age group with a mean age of 6.9 years, and six were adults with a mean age of 55.2 years. Thirteen patients were on different chemotherapy protocols, and one was undergoing an autologous stem cell transplant. Of the 14 patients, four were asymptomatic for COVID-19 symptoms, eight had mild disease, and two had severe disease with respiratory failure. Two patients with severe diseases needed COVID-19-designated high-dependency unit (HDU) admission. There was one COVID-19-related death. Among the healthcare workers, the mean age was 33.8 years, of which six were males and three were females. All the HCWs and NHCWs had mild disease, and all of them recovered completely. CONCLUSION: Nosocomial COVID-19 illness is a new entity and is preventable. COVID-19 illness will remain in society after the pandemic is over, like the influenza B viral illness, and there can be seasonal flares in the future. Proper measures should be taken to prevent its clustering in hospital settings.

Experimental and in silico evaluation of Carthamus tinctorius L. oil emulgel: a promising treatment for bacterial skin infections.

Purpose: The current study aimed to develop a topical herbal emulgel containing Carthamus tinctorius L. (CT) oil extract, which has been scientifically proven for its antibacterial and antioxidant activities for the ailment of bacterial skin infections. Method: The CT emulgel was formulated by response surface methodology (RSM) and was evaluated by various parameters like extrudability, spreadability, pH, viscosity, and antibacterial and antioxidant activities. Molecular docking was also performed using AutoDock. Results: Among all formulated CT emulgels, F9 and F8 were optimized. Optimized formulations had shown good spreadability and extrudability characteristics. Sample F8 had % inhibition of 42.131 ± 0.335, 56.720 ± 0.222, and 72.440 ± 0.335 at different concentrations. Sample F9 had % inhibition of 26.312 ± 0.280, 32.461 ± 0.328, and 42.762 ± 0.398 at concentrations of 250 µg/ml, 500 µg/ml, and 1,000 µg/ml, respectively, which shows that both samples F8 and F9 have significant antioxidant potential. Optimized CT emulgels F8 and F9 had significant antibacterial activity against Staphylococcus aureus and Escherichia coli at p-value = 0.00, the Emulgel-F8 shows zone of inhibition of 24 mm for E-coli and 19 mm for S-aureus. Emulgel-F9 shows zone of inhibition of 22 mm for E-coli and 15 mm for S-aureus while pure CT- Oil extract shows zone of inhibition of 25 mm for E-coli and 20 mm for S-aureus and ciprofloxacin used as standard shows 36mm zone of inhibition against both E-coli and S-aureus. The comparative investigation through molecular docking binding affinities and interactions of ligands with various target proteins provides insights into the molecular processes behind ligand binding and may have significance for drug discovery and design for the current study. Conclusion: The current study suggests that C. tinctorius L.-based emulgel has good antioxidant and antibacterial activities against E. coli for the treatment of bacterial skin infections.

Clinicopathological Profile of Non-small Cell Lung Cancer and the Changing Trends in Its Histopathology: Experience From a Tertiary Care Cancer Center in Kashmir, India.

Background The overall frequency and incidence of different cancers across the globe, including lung cancer, are marked by ethnic and geographical variations. Lung cancer is the most commonly diagnosed cancer worldwide that inflicts most of the cancer deaths. Non-small cell lung cancer (NSCLC) constitutes most lung cancer cases. The aim of this study was to find the frequency and clinicopathological characteristics of NSCLC in high incidence zone of Kashmir, an ethnically and geographically distinct area in Northern India. Material and methods The study was conducted to evaluate the clinicopathological profile of NSCLC at a tertiary care cancer center, Sher-I-Kashmir Institute of Medical Sciences (SKIMS). The patients and case records were analyzed for clinical presentation and demographic features, smoking status, radiological features, histopathological type, and stage of their disease at presentation. Results The study included 1557 NSCLC patients registered over a period of seven years, i.e., 2008-2014. Most of the patients belonged to rural areas (70%). The median age of the whole cohort was 58.0 years (ranges 22-95 years), and the male-to-female ratio was 3.7:1 (male = 1231 and female = 326). Smokers comprised 77.39% of cases, and Hookah was the most common form of smoking (65.06%). The ratio of squamous cell carcinoma and adenocarcinoma was 3.7:1 (67.5% vs. 24.9%). Stage III and IV disease accounted for 93% of cases (30.6% and 62.7%, respectively). Squamous cell carcinoma histopathology was dominant in smokers (74.3%) compared to adenocarcinoma (19%), while squamous cell carcinoma and adenocarcinoma histopathology ran parallel in nonsmokers (45.1% and 44%, respectively). Most of the patients had an Eastern Cooperative Oncology Group (ECOG) performance status between 1 and 2 (79%).  Conclusion We conclude Kashmir region is a very high-risk area for lung cancer, with NSCLC showing a high incidence. Most of our patients present in advanced stages, and the frequency of adenocarcinoma is showing an increasing trend over the years from 2008-2014.

Predictors of Rebleeding in Non-variceal Upper Gastrointestinal Bleeding of Peptic Ulcer Etiology in Kashmiri Population.

Background Upper gastrointestinal bleeding (UGIB) represents a substantial clinical and economic burden and rebleeding is one of the most important predictors of morbidity and mortality. Identifying patients who are likely to rebleed is a critical component of effectively managing patients with bleeding peptic ulcers. So, the study was undertaken to look for predictors of rebleeding in patients with bleeding peptic ulcers and try to find out the new scoring system to predict rebleeding in our population. Material and methods A retrospective analysis of prospectively maintained hospital data of UGIB patients was done and 480 patients of endoscopically documented peptic ulcers whose complete data was available were taken for study. Results Among the studied patients, men constituted 84.6%, and most of the patients were in the third to sixth decade of life with a mean age of 40.9±15.9 years, 76% were from rural areas. Only males with a mean age of 38.4±19.8 rebled with a rebleeding rate of 2.9% only. Half of the patients who rebled were in shock at the time of presentation. Those who rebled received more units of blood transfusion (mean 3±1.8), had a large mean ulcer size of Forest class IIa and IIb and epinephrine injection monotherapy group with varied statistical significance. Among rebleeders (n=14), eight patients were managed by a second endoscopic therapy, and six (42.8%) rebleeders and 1.25% of patients in total needed surgery. Two patients ultimately died giving overall mortality of 0.4% and mortality of 14.3% among rebleeders. Conclusion Our study found a very low rebleeding rate and mortality which could be explained by a young population with fewer co-morbidities and better response to proton pump inhibitor therapy. The significant parameters related to rebleeding were shock at presentation, degree of smoking, units of blood transfused, ulcer size, and high-risk endoscopic stigmata.

A Hematological and Histopathological Study on Diphenhydramine Nasal Nano-gel and Nano-emulgel for the Management of Allergic Rhinitis in Animal Model.

The study aims to determine histamine efficacy on hematologic values in experimental animal model, under physiological and pathological conditions after inducing diphenhydramine-formulated nasal nano-gel/nano-emulgel in comparison with conventional nasal spray regime. In this study, we conducted experiment on New Zealand white male rabbits to prove our hypothesis that nasal diphenhydramine nano-gel and nano-emulgel can penetrate the nasal mucosa faster to show drug response and subside histaminic symptoms than market nasal spray (as reference). Blood samples from 48 New Zealand white male rabbits, under both experimental conditions (physiological and pathological) divided into four groups for each (n = 6) were investigated after inducing each dosage form intranasally. Hematologic parameters (WBCs, RBCs, HGB, PLTs, lymphocytes, monocytes, eosinophils, granulocyte counts) were analyzed in whole blood samples, collected at different time intervals. ANOVA and completely randomized design (CRD) were applied for statistical analysis. Histopathologically, nasal tissues of all groups were analyzed to see intramucosal surface changes. Data of descriptive statistics of hematological parameters analyzed at confidence level 95% showed that under physiological condition, hematological parameters of all groups were lying in normal range, whereas under pathological condition, low values of all hematological parameters were observed in all groups due to allergenic condition. The groups B (allergenic rabbits treated with formulated diphenhydramine nasal nano-gel) and C (allergenic rabbits treated with formulated diphenhydramine nasal nano-emulgel) have shown good changes in the treatment of allergenic rabbits as compared to group D (allergenic rabbits treated with formulated diphenhydramine nasal spray). The completely randomized ANOVA and Tukey HSD all-pairwise comparison tests of hematological parameters were applied that showed all groups in both studies were significantly different from each other. It was observed after histopathological study of nasal membrane tissues that change in mucosa has occurred due to the passage of drug. In summary, hematological profile and histopathological study have demonstrated the comparable results with conventional diphenhydramine nasal spray and formulated diphenhydramine nasal nano-gel/nano-emulgel which can exhibit considerable drug delivery dosage forms in the management of allergic rhinitis in animal model.

Assessment of formulation variables of poor water soluble diacerein for its improved loading and anti-inflammatory activity.

Dissolving microneedles have become a popular method for percutaneous administrationof drugs. However, loading poorly soluble drugs into water-based dissolving microneedles remains a challenge. In view of this, we aimed to improve Diacerein (DCN) solubility formulating dissolving microneedles. DCN microsuspension was created by high-speed homogenization with organic solvents or wet milling with Tween 80 as a stabilizer (LD1). They were analyzed for particle size and saturation solubility. Subsequently, the organic solvent-based microneedles were prepared under vacuum, whereas LD1 was mixed with HPMC (8% w/w) and PVP (30% w/w) matrix to concentrate the drug in acral fraction through centrifugation. DCN microsuspension in DMSO had the highest drug solubility with an average particle size of 6 µm, whereas LD1 had a particle size of 3.28 µm showing improved solubility. TD-3 had the highest drug loading and the least amount of drug migration into the blank baseplate. Within 5 min, these microneedles dissolved completely in an agarose-gel block. LD1 was likewise put in the baseplate to generate TD3-B. Within 24 h, 74.39% of the medication was released from TD3-B, with only a small amount remaining in the baseplate. TLC examination indicated the conversion of DCN to Rhein in the skin, whereas DSC and TGA studies revealed amorphous features. DCN microneedles showed no sign of skin irritancy but showed anti-inflammatory response on carrageenan-induced paw edema model. Microneedles remained stable during accelerated stability testing. Wet milling in the presence of a stabilizer can be an effective approach for enhancing DCN solubility for improved drug loading in dissolving microneedles. Improvement in solubility of Diacerein for subsequent loading in Dissolving Microneedle for percutaneous delivery.

Patterns of Treatment and Outcomes in Epithelial Ovarian Cancer: A Retrospective North Indian Single-Institution Experience.

PURPOSE: Ovarian cancer (OC) is ranked as the third most common gynecologic cancer in various Indian cancer registries. In India, OC is seen in the younger age group, with a median age < 55 years being reported by most of the studies. The majority of patients are diagnosed in advanced stage (70%-80%), where the long-term (10-year) survival rate is poor, estimated at 15%-30%. The aim of this study was to evaluate clinical epidemiology, treatment patterns, and survival outcomes in patients with epithelial OC. METHODS: This was a retrospective analysis of patients with epithelial OC who were treated at Sher-i-Kashmir Institute of Medical Sciences, Srinagar, over a period of 9 years, from January 2010 to December 2018. RESULTS: OC constituted 2.94% of all cancers registered. Epithelial OC constituted 88.4% of all OCs, with a median age 50 years. More than two third of patients belonged to rural background and the majority (76.9%) of the patients were in stage III or IV at the time of diagnosis. The main presenting symptoms were abdominal distension/bloating (46.5%) and gastrointestinal disturbances (35.2%). The most common histologic types were serous (65.9%) followed by mucinous carcinoma (15%). Median overall survival for the whole study cohort was 30 months (95% CI, 28.0 to 31.9). Median overall survival for stage I, II, III, and IV was 72, 60, 30, and 20 months, respectively. CONCLUSION: Most of the patients presented in advanced stage of the disease and have poor outcome. Delay in diagnosis and improper management before registering in tertiary cancer center and lack of tertiary care facilities are the root causes of poor outcomes. The general population and primary care physicians need to be made aware of OC symptoms.

Amidated Pluronic Decorated Muco-Penetrating Self-Nano Emulsifying Drug Delivery System (SNEDDS) for Improved Anti-Salmonella typhi Potential.

The enteric system residing notorious Salmonella typhimurium (S. typhi) is an intracellular, food-borne, and zoonotic pathogen causing typhoid fever. Typhoid fever is one of the leading causes of mortality and morbidity in developing and underdeveloped countries. It also increased the prevalence of multidrug resistance globally. Currently, available anti-bacterial modalities are unable to penetrate into the intracellular compartments effectively for eradicating S. typhi infection. Therefore, in this study, we developed nanostructured lipid-based carriers in the form of a self-nanoemulsifying drug delivery system (SNEDDS) for targeted delivery of ciprofloxacin (CIP) into the S. typhi intracellular reservoirs. Capryol 90, Tween 80, and Span 20 were finalized as suitable oil, surfactant, and co-surfactant, respectively, according to the pseudoternary phase diagram emulsifying region. Targeting capability and mucopenetration of the SNEDDS was attributed to the inclusion of amidated pluronic (NH2-F127). Developed NH2-F127 SNEDDS were characterized via physicochemical, in vitro, ex vivo, and in vivo evaluation parameters. The size of the SNEDDS was found to be 250 nm, having positively charged zeta potential. In vitro dissolution of SNEDDS showed 80% sustained release of CIP in 72 h with maximum entrapment efficiency up to 90% as well as good hemocompatibility by showing less than 0.2% hemolysis and 90% biocompatibility. The survival rate of S. typhi in macrophages (RAW 264.7) was minimal, i.e., only 2% in the case of NH2-F127 SNEDDS. Macrophage uptake assay via nanostructures confirmed the maximum cellular uptake as evidenced by the highest fluorescence. Biofilm dispersion assay showed rapid eradication of developed resistant biofilms on the gall bladder. In vivo pharmacokinetics showed improved bioavailability by showing an increased area under the curve (AUC) value. Taken together, NH2-F127-SNEDDS can be utilized as an alternative and efficient delivery system for the sustained release of therapeutic amounts of CIP for the treatment of S. typhi.

Formulation development and evaluation of transdermal emulgel using Aloevera extract and natural penetration enhancers.

This study was to formulate Aloevera extract loaded emulsion (O/W) based gels, by using various concentrations of rose oil, olive oil and Lemon oil as natural penetration enhancers for transdermal effect to treat skin problems. By using RSM, Aloevera emulgels were formulated and then optimized. Stability studies, physico-chemical characteristics, spreadability, skin protection factor, thermal analysis, FTIR, antimicrobial activity, in vitro drug release study (at 37ºC with 100 rpm for 180 minute in release medium at pH 5.5) and in vivo skin evaluation tests were performed. The results were then statistically analyzed. Among all formulations, G12 has shown maximum 93.53% Aloevera release at higher concentration of Olive oil with decreased concentration of Rose oil and Lemon oil. Analysis of variance (ANOVA) was conducted to evaluate the results exhibited independent variables have remarkable effects on dependent variables. Contour plot is also drawn to express the response between independent and dependent variables. All formulations have followed Korsmeyer-Peppas kinetic model. In summary, the combination of penetration enhancers in Aloevera emulgel can be successfully utilized for treatment of mild-moderate acne vulgaris and other skin problems, as optimized emulgel has shown good permeability, prolonged residence time on skin surface and proved good anti-microbial activity.

Effectiveness of Cognitive Restructuring on Intensity of Pain in Cancer Patients: A Pilot Study in Oncology Department of Tertiary Care Hospital.

BACKGROUND: Pain is a most feared symptom among cancer patients. It not only affects physical health but it is a psychological burden and affects overall quality of life in cancer patients. it interferes the activity of daily living and treatment outcome. cognitive restructuring is very useful psychological treatment to reduce pain. OBJECTIVES: The purpose of the study was to assess level of pain, evaluate effectiveness of cognitive restructuring on intensity of pain and to find association of pain with demographic variables. METHODS: Quasi experimental  study was conducted in oncology department of SKIMS tertiary care hospital. Purposive sampling technique was used to select 22 patients for study group and 22 patients for control group. CBPS and Numerical rating pain scale was used to measure intensity of pain. Data collected by interview method. RESULTS: The results showed significant difference p<0.05 at only  in anxiety,face and activity on CBPS scores. Mean score of NPRS was 27.27% of moderate pain  and severe pain reduced from 63.64 to  to 0.%  in study group after CR. Results revealed Significant association of pain with age, gender and period of illness. CONCLUSION: Based on findings of the study it is concluded that CR has significant impact in cancer patients  on reducing pain and can ease problems related to pain. CR is an appropriate intervention to reduce the symptoms of cancer patients which has indirect impact on cancer treatment.

Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates.

Zika fever is an infectious disease caused by the Zika virus (ZIKV). The disease is claiming millions of lives worldwide, primarily in developing countries. In addition to vector control strategies, the most effective way to prevent the spread of ZIKV infection is vaccination. There is no clinically approved vaccine to combat ZIKV infection and curb its pandemic. An epitope-based peptide vaccine (EBPV) is seen as a powerful alternative to conventional vaccinations because of its low production cost and short production time. Nonetheless, EBPVs have gotten less attention, despite the fact that they have a significant untapped potential for enhancing vaccine safety, immunogenicity, and cross-reactivity. Such a vaccine technology is based on target pathogen's selected antigenic peptides called T-cell epitopes (TCE), which are synthesized chemically based on their amino acid sequences. The identification of TCEs using wet-lab experimental approach is challenging, expensive, and time-consuming. Therefore in this study, we present computational model for the prediction of ZIKV TCEs. The model proposed is an ensemble of decision trees that utilizes the physicochemical properties of amino acids. In this way a large amount of time and efforts would be saved for quick vaccine development. The peptide sequences dataset for model training was retrieved from Virus Pathogen Database and Analysis Resource (ViPR) database. The sequences dataset consist of experimentally verified T-cell epitopes (TCEs) and non-TCEs. The model demonstrated promising results when evaluated on test dataset. The evaluation metrics namely, accuracy, AUC, sensitivity, specificity, Gini and Mathew's correlation coefficient (MCC) recorded values of 0.9789, 0.984, 0.981, 0.987, 0.974 and 0.948 respectively. The consistency and reliability of the model was assessed by carrying out the five (05)-fold cross-validation technique, and the mean accuracy of 0.97864 was reported. Finally, model was compared with standard machine learning (ML) algorithms and the proposed model outperformed all of them. The proposed model will aid in predicting novel and immunodominant TCEs of ZIKV. The predicted TCEs may have a high possibility of acting as prospective vaccine targets subjected to in-vivo and in-vitro scientific assessments, thereby saving lives worldwide, preventing future epidemic-scale outbreaks, and lowering the possibility of mutation escape.

Synthesis and characterization of self-assembling chitosan-based nanoparticles.

Chitosan (CHT) based biodegradable nanovectors were synthesized and modified with poly ethylene glycol 4000 (PEG-4000). CHT having medium molecular weight with 75% to 85% deacetylation was phthaloylated with phthalic anhydride, followed by PEGylation using PEG-4000. After confirmation of successful PEGylation by fourier transforminfra red spectroscopy (FTIR), the modified polymer was further processed to develop the nanocarrier using ionic gelation method by the addition of sodium tripolyphosphate (NaTPP). The prepared nanocarriers were subjected to physicochemical evaluation. The surface morphology of the particles was observed under scanning electron microscope (SEM), and particle size by dynamic light scattering (DLS) method, which was about 159-170nm in diameter. The zeta potential of the prepared nanovectors was +0.907mV which was due to cationic nature of nanovectors. The cell viability studies were also conducted to find the suitability of the carrier for in-vivo application, using liver cancerous cells (Hep G2). The findings have disclosed the concentration dependent activities of the particles, as viability of the cell was shown to be decreased with the increase in the concentration of the particles. Conclusively, the study was successful in determining the toxicity profile of these nanovectors as these were proved non-toxic at specific concentration.

Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review.

The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety-they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.

Ensemble Machine Learning Model to Predict SARS-CoV-2 T-Cell Epitopes as Potential Vaccine Targets.

An ongoing outbreak of coronavirus disease 2019 (COVID-19), caused by a single-stranded RNA virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide pandemic that continues to date. Vaccination has proven to be the most effective technique, by far, for the treatment of COVID-19 and to combat the outbreak. Among all vaccine types, epitope-based peptide vaccines have received less attention and hold a large untapped potential for boosting vaccine safety and immunogenicity. Peptides used in such vaccine technology are chemically synthesized based on the amino acid sequences of antigenic proteins (T-cell epitopes) of the target pathogen. Using wet-lab experiments to identify antigenic proteins is very difficult, expensive, and time-consuming. We hereby propose an ensemble machine learning (ML) model for the prediction of T-cell epitopes (also known as immune relevant determinants or antigenic determinants) against SARS-CoV-2, utilizing physicochemical properties of amino acids. To train the model, we retrieved the experimentally determined SARS-CoV-2 T-cell epitopes from Immune Epitope Database and Analysis Resource (IEDB) repository. The model so developed achieved accuracy, AUC (Area under the ROC curve), Gini, specificity, sensitivity, F-score, and precision of 98.20%, 0.991, 0.994, 0.971, 0.982, 0.990, and 0.981, respectively, using a test set consisting of SARS-CoV-2 peptides (T-cell epitopes and non-epitopes) obtained from IEDB. The average accuracy of 97.98% was recorded in repeated 5-fold cross validation. Its comparison with 05 robust machine learning classifiers and existing T-cell epitope prediction techniques, such as NetMHC and CTLpred, suggest the proposed work as a better model. The predicted epitopes from the current model could possess a high probability to act as potential peptide vaccine candidates subjected to in vitro and in vivo scientific assessments. The model developed would help scientific community working in vaccine development save time to screen the active T-cell epitope candidates of SARS-CoV-2 against the inactive ones.