Assessment of occlusion changes during laboratory phase of relining: An in vitro study.

AIM: Assessment of occlusion changes during laboratory phase of relining is essential to evaluate the occlusal discrepancies that could get incorporated in the denture with the use of different relining materials. Since the long term stability and functional success of the denture is heavily influenced by occlusion, an In-vitro study to assess these changes after relining is warranted. The aim of the study is to evaluate the changes in occlusion during laboratory phase of relining procedure. SETTINGS AND DESIGN: This is an in vitro study with a total of 30 specimen. MATERIALS AND METHODOLOGY: A total of 30 maxillary standardized dentures were fabricated after mounting on a semi adjustable articulator. These samples will be divided into three groups based on the relining material used (Autopolymerizing resin, Heat-cure resin, Tissue conditioner). The vertical dimension, Centric contact points and eccentric contact points were measured before and after relining. STATISTICAL ANALYSIS USED: The variables were tested to see if they had a normal distribution using the Shapiro-Wilk test. Parametric distribution was seen for ECP leading to further comparison using one way analysis of variance (ANOVA). Non-parametric distribution was found while testing the VD, CCP leading to adoption of Kruskal-wallis test for comparison of groups. Dunn Bonferroni test was done for VD since results were significant. RESULTS: The results of this in-vitro study showed statistically significant difference with respect to change in vertical dimension in all groups pre and post relining (P = 0.005). The centric contact points showed lesser variation in position when comparing the pre to the post relining phase with the use of autopolymerising resins, whereas heat cure resins and tissue conditioners showed statistically significant difference in the centric point contacts post relining. No statistically significant changes were seen in eccentric occlusion post relining in all groups. Tissue conditioners showed minimum mean changes in eccentric contacts. CONCLUSION: Within the limitations of this study, the use of autopolymerising resins depicted the most stable results with respect to occlusion, for relining of dentures.

Evolving Horizons: Adenovirus Vectors' Timeless Influence on Cancer, Gene Therapy and Vaccines.

Efficient and targeted delivery of a DNA payload is vital for developing safe gene therapy. Owing to the recent success of commercial oncolytic vector and multiple COVID-19 vaccines, adenovirus vectors are back in the spotlight. Adenovirus vectors can be used in gene therapy by altering the wild-type virus and making it replication-defective; specific viral genes can be removed and replaced with a segment that holds a therapeutic gene, and this vector can be used as delivery vehicle for tissue specific gene delivery. Modified conditionally replicative-oncolytic adenoviruses target tumors exclusively and have been studied in clinical trials extensively. This comprehensive review seeks to offer a summary of adenovirus vectors, exploring their characteristics, genetic enhancements, and diverse applications in clinical and preclinical settings. A significant emphasis is placed on their crucial role in advancing cancer therapy and the latest breakthroughs in vaccine clinical trials for various diseases. Additionally, we tackle current challenges and future avenues for optimizing adenovirus vectors, promising to open new frontiers in the fields of cell and gene therapies.

A Comparison of Occlusal Schemes with Condylar Inclination and Anterior Guidance in Dentate Individuals.

PURPOSE: A Comparison of Occlusal Schemes with Condylar Inclination and Anterior Guidance in Dentate Individuals Methods. Twenty-six dentate patients between the ages of 18 to 30 of Indian Origin with canine-guided occlusion and 26 patients with group function occlusion were included in the study. The School of Articulator Munich (SAM) Axioquick system was used to assess the condylar guidance and eccentric tracings of the patients. For analyses, Student's t-test was used. For quantitative data, the mean and standard deviation were calculated. For all the statistical analysis the probability of type-I error of 0.05 was considered statistically significant. RESULTS: The mean condylar guidance for canine guided and group function occlusion on the right side was 38.4 ±12.7 and 30.5 ±12.5 and on the left side was 36.5 ±13.0 and 27.5±12.0 degrees with statistically significant difference [P value: 0.01]. The condylar guidance, incisal guidance, Bennett angle, protrusion, left lateral, right lateral, and left and right lateral angles were analyzed statistically between the two types of occlusions. The results showed a statistically significant difference between the two groups for all parameters except right condylar guidance. Furthermore, all the parameters were higher in canine guided occlusion group over group function occlusion. CONCLUSIONS: Within the study limitations, it was concluded that the condylar guidance was steeper in canine guided occlusion than in group function occlusion. The eccentric parameters were steeper in canine guided occlusion than in group function occlusion. CLINICAL SIGNIFICANCE: This study showcases that the condylar guidance might not play a major role in determining the occlusal scheme but, the incisal guidance determines the occlusal scheme predominantly in class 1 occlusion patients.

Current avenues of gene therapy in Pompe disease.

PURPOSE OF REVIEW: Pompe disease is a rare, inherited, devastating condition that causes progressive weakness, cardiomyopathy and neuromotor disease due to the accumulation of glycogen in striated and smooth muscle, as well as neurons. While enzyme replacement therapy has dramatically changed the outcome of patients with the disease, this strategy has several limitations. Gene therapy in Pompe disease constitutes an attractive approach due to the multisystem aspects of the disease and need to address the central nervous system manifestations. This review highlights the recent work in this field, including methods, progress, shortcomings, and future directions. RECENT FINDINGS: Recombinant adeno-associated virus (rAAV) and lentiviral vectors (LV) are well studied platforms for gene therapy in Pompe disease. These products can be further adapted for safe and efficient administration with concomitant immunosuppression, with the modification of specific receptors or codon optimization. rAAV has been studied in multiple clinical trials demonstrating safety and tolerability. SUMMARY: Gene therapy for the treatment of patients with Pompe disease is feasible and offers an opportunity to fully correct the principal pathology leading to cellular glycogen accumulation. Further work is needed to overcome the limitations related to vector production, immunologic reactions and redosing.

Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture.

Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data-based analyses are able to identify these events, allowing RNA-sequence and microarray cancer data to be used for cancer analyses. Breast cancer is the most common cancer worldwide, and is classified into four stages - stages 1, 2, 3, and 4 [2]. While machine learning models have previously been explored to perform stage classification with limited success, multi-class stage classification has not had significant progress. There is a need for improved multi-class classification models, such as by investigating deep learning models. Gene-expression-based cancer data is characterised by the small size of available datasets, class imbalance, and high dimensionality. Class balancing methods must be applied to the dataset. Since all the genes are not necessary for stage prediction, retaining only the necessary genes can improve classification accuracy. The breast cancer samples are to be classified into 4 classes of stages 1 to 4. Invasive ductal carcinoma breast cancer samples are obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets and combined. Two class balancing techniques are explored, synthetic minority oversampling technique (SMOTE) and SMOTE followed by random undersampling. A hybrid feature selection pipeline is proposed, with three pipelines explored involving combinations of filter and embedded feature selection methods: Pipeline 1 - minimum-redundancy maximum-relevancy (mRMR) and correlation feature selection (CFS), Pipeline 2 - mRMR, mutual information (MI) and CFS, and Pipeline 3 - mRMR and support vector machine-recursive feature elimination (SVM-RFE). The classification is done using deep learning models, namely deep neural network, convolutional neural network, recurrent neural network, a modified deep neural network, and an AutoKeras generated model. Classification performance post class-balancing and various feature selection techniques show marked improvement over classification prior to feature selection. The best multiclass classification was found to be by a deep neural network post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with a Cohen-Kappa score of 0.303 and a classification accuracy of 53.1%. For binary classification into early and late-stage cancer, the best performance is obtained by a modified deep neural network (DNN) post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with an accuracy of 81.0% and a Cohen-Kappa score (CKS) of 0.280. This pipeline also showed improved multiclass classification performance on neuroblastoma cancer data, with a best area under the receiver operating characteristic (auROC) curve score of 0.872, as compared to 0.71 obtained in previous work, an improvement of 22.81%. The results and analysis reveal that feature selection techniques play a vital role in gene-expression data-based classification, and the proposed hybrid feature selection pipeline improves classification performance. Multi-class classification is possible using deep learning models, though further improvement particularly in late-stage classification is necessary and should be explored further.

Unaided visual assessment of ventral curvature during hypospadias repair is inferior to objective measurement using app goniometry.

OBJECTIVE: Failure to perform artificial erection or objectively assess ventral curvature (VC) during primary hypospadias repair is an important reason for residual/ recurrent chordee. The present study compares the accuracy of unaided visual inspection (UVI) with objective VC assessment using smartphone application (app) goniometry. METHODS: All patients who underwent primary hypospadias repair between January 2021 and September 2022 were included. Assistant surgeons were asked to grade the degree of VC on UVI (after degloving and an artificial erection test) into: none, mild (<30 degree), severe(>30 degree). Lateral profile photograph was taken and angle measurement was performed on an android mobile application (Angulus). Correlation was performed with both methods of assessment. RESULTS: During this period a total of 210 patients were analyzed; VC was noted in 40/138 (29%) cases of distal and in 62/72 (86%) cases of proximal hypospadias. Erroneous visual inspection was noted in 41/210 (20%; 95% CI 14-25%) on UVI (15 erroneously marked none while 26 marked mild). Among those found to have chordee, UVI assessed 39/82 (47%) as severe while app goniometry assessed 65/97 (67%) as severe. There was significant relative risk of labelling severe chordee as a mild one by UVI: 1.4 (95%CI 1-1.8; p=0.01). CONCLUSIONS: UVI was erroneous in 20% of cases. UVI was less accurate in differentiating severe chordee from mild one. In 60% patients UVI alone could have led to erroneous VC assessment and thus wrong selection of technique. Further studies are required to validate our findings and standardize VC measurement using an app goniometry.

Self-isolated MIMO antenna using mixed coupling by close coupling technique.

A self-isolated multiple-input-multiple-output (MIMO) antenna in a compact shared ground structure is proposed for 5G systems. The proposed MIMO antenna consists of customized M-pattern, closely coupled members. It benefits to attain good isolation of the targeted bandwidth transversely without additional de-coupling structures. It is discovered that the arm of M-pattern antenna members can cancel out the coupling on the system and achieve sound isolation among antenna members. A relevant matching circuit model is discussed to show how the suggested theory works in principle. The mixed couplings among antenna members neutralize by modifying the antenna shape with the help of electric and magnetic coupling and surface currents. The proposed self-isolated 2-member MIMO antenna demonstrates sound isolation superior to 15 dB transversely in the frequency bands dedicated to 5G NR: n48/n78 and long-term evolution (LTE) band 42/43/48/52 (3.2-3.98 GHz). Moreover, the proposed 2-member design structure has a scalability advantage. It is extended into an 8-members structure, where a pair of antennas located at each side of the frame offers orthogonality. The proposed 8-members M-pattern MIMO is validated using fabricated and simulated measurements. The investigational results show that the 8-members MIMO (M-shaped) system is effective in higher order MIMO antenna design and offers more than 20 dB isolation transversely in the frequency band 5G NR n48 and LTE band 42/43/52(3.29-3.66 GHz).

Analysis of chemical engineering curriculum to improve process safety competency.

Continuous process safety (PS) development is the key to maintaining a good PS system, and its competency plays a substantial role. However, PS incompetency can still be demonstrated in several process-related accidents, particularly major catastrophic incidents. To mitigate this gap, universities' PS education is analysed. Because PS is an important element of chemical engineering (CE), this study seeks to identify the most prevalent PS subjects taught in the top 300 Quacquarelli Symonds ranking (2019) universities. Findings indicate that PS education remains insufficiently addressed in undergraduate CE curricula over the years. Twelve common topics, i.e., human factors; management of hazards, incidents, and risk; design; fire and explosion; legislation and standards; sustainability; process control; economics; toxicology; and software are identified. Notably, sustainability is acknowledged to be a new common PS topic, depicting its demand for industrial evolution. Ultimately, strengthening the collaboration between universities and industries is required to develop graduates' PS competency.Abbreviations: ALARP: as low as reasonably practicable; CAD: computer-aided design; CE: chemical engineering; ETA: event tree analysis; FTA: fault tree analysis; FMEA: failure mode and effect analysis; HAZAN: hazard analysis; HAZID: hazard identification; HAZOP: hazard and operability; HSE: health, safety and environment; HYSYS: Hyprotech Systems; LCA: life cycle analysis; LOPA: layer of protection analysis; MS: Microsoft; ORP: occupational risk prevention; PC: personal computer; PHA: process hazard analysis; PS: process safety; PSM: process safety management; QS: Quacquarelli Symonds; SMS: safety management system.

Impact of Fasting during Ramadan on Renal Functions in Patients with Chronic Kidney Disease.

Introduction: The impact of Ramadan fasting in patients with chronic kidney disease (CKD) remains less studied and with inconsistent results. In this study, we tried to look at the impact of Ramadan fasting on renal function in patients with CKD. Materials and Methods: In this prospective observational study, we included 28 adult CKD patients. All relevant biochemical parameters including renal function tests were done in the month before Ramadan fasting and within 3 months after Ramadan. Urine output, body weight, and blood pressure were also monitored during Ramadan and after the end of Ramadan for at least 10 days. Results: All the 28 patients (mean age: 46 ± 12 years) included in the study managed to fast for the whole month, and none displayed any new clinical symptoms or signs. The renal function worsened in four (14.28%), and it was significant in those with CKD Stages 4 and 5 (P < 0.003). Conclusion: Stable CKD patients can fast with careful monitoring; however, there is a risk of renal function deterioration in advanced CKD.

Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques.

Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. Lung diseases such as tuberculosis (TB), bacterial and viral pneumonias, and COVID-19 are not predicted accurately due to availability of very few samples for either of the lung diseases. The disease could be easily diagnosed using X-ray or CT scan images. But the number of images available for each of the disease is not as equally as other resulting in imbalance nature of input data. Conventional supervised machine learning methods do not achieve higher accuracy when trained using a lesser amount of COVID-19 data samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation helped reduce overfitting when training a deep neural network. The SMOTE (Synthetic Minority Oversampling Technique) algorithm is used for the purpose of balancing the classes. The novelty in this research work is to apply combined data augmentation and class balance techniques before classification of tuberculosis, pneumonia, and COVID-19. The classification accuracy obtained with the proposed multi-level classification after training the model is recorded as 97.4% for TB and pneumonia and 88% for bacterial, viral, and COVID-19 classifications. The proposed multi-level classification method produced is ~8 to ~10% improvement in classification accuracy when compared with the existing methods in this area of research. The results reveal the fact that the proposed system is scalable to growing medical data and classifies lung diseases and its sub-types in less time with higher accuracy.

Analysis and prediction of water quality using deep learning and auto deep learning techniques.

Natural water sources like ponds, lakes and rivers are facing a great threat because of activities like discharge of untreated industrial effluents, sewage water, wastes, etc. It is mandatory to examine the water quality to ensure that only safe water is available for consumption. Traditional methods of water quality inspection are a cumbersome process and hence, Artificial Intelligence (AI) can be used as a catalyst for this process. AutoDL is an upcoming field to automate deep learning pipelines and enables model creation and interpretation with minimal code. However, it is still in the nascent stage. This work explores the suitability of adopting AutoDL for Water Quality Assessment by drawing a comparison between AutoDL and a conventional models and analysis to foresee the quality of the water, an appropriate class based on Water Quality Index segregating water bodies into different classes. The accuracy of conventional DL is 1.8% higher than that of AutoDL for binary class water data. The accuracy of conventional DL is 1% higher than that of AutoDL for multiclass water data. The accuracy of conventional model was ~98% to ~99% whereas AutoDL method yielded ~96% to ~98%. However, the AutoDL model ease the task of finding the appropriate DL model and proved better efficiency without manual intervention.

Comparison of highly pure rAAV9 vector stocks produced in suspension by PEI transfection or HSV infection reveals striking quantitative and qualitative differences.

Recent clinical successes have propelled recombinant adeno-associated virus vectors (rAAV) to the center stage for human gene therapy applications. However, the exploding demand for high titers of highly pure rAAV vectors for clinical applications and market needs remains hindered by challenges met at the manufacturing stage. The production of rAAV by transfection in suspension cells remains one of the most commonly used production platforms. In this study, we describe our optimized protocol to produce rAAV by polyethyleneimine (PEI)-mediated transfection in suspension HEK293 cells, along with a side-by-side comparison to our high-performing system using the herpes simplex virus (HSV). Further, we detail a new, robust, and highly efficient downstream purification protocol compatible with both transfection and infection-based harvests that generated rAAV9 stocks of high purity. Our in-depth comparison revealed quantitative, qualitative, and biological differences between PEI-mediated transfection and HSV infection. The HSV production system yielded to higher rAAV vector titers, higher specific yields, and a higher percentage of full capsids than transfection. Furthermore, HSV-produced stocks had a significantly lower concentration of residual host cell proteins and helper DNA impurities, but contained detectable levels of HSV DNA. Importantly, the potency of PEI-produced and HSV-produced rAAV stocks were identical. Analyses of AAV Rep and Cap expression levels and replication showed that HSV-mediated production led to a lower expression of Rep and Cap, but increased levels of AAV genome replication. Our methodology enables high-yield, high purity rAAV production and a biological framework to improve transfection quality and yields by mimicking HSV-induced biological outcomes.

Addressing process safety challenges in downstream industries in Brunei Darussalam.

International oil and gas corporations operating in Brunei may apply process safety management (PSM) and analysis techniques, resulting in varying approaches and measures to address process safety issues. Global corporations may have developed their own process safety standards while smaller firms employ established ones. This research compares the local PSM systems and standards with international ones to determine which employers face the most difficulties in implementing or increasing process safety inside their organizations. This study found that Occupational Safety and Health Administration (OSHA) regulations are used by 30% of local users in downstream operations. Common challenges encountered by local users are management/leadership commitment to process safety (11.9%), mechanical integrity and management of safety critical devices (5.3%), management review and intervention for continuous improvement (4.9%), communication amongst workers (3.8%), management of change (3.8%), operational control, permit to work and risk management (3.8%) and incident reporting (3.8%).

Emergency response plan for methane and chlorine with dispersion modelling using CAMEO.

There is a significant need in the current industrial scenario for methods to be formulated to treat dangerous chemicals most safely. Accidental release of toxic chemicals will result in emergencies. Hence, an emergency response plan (ERP) is inevitable. The most toxic chemicals in the water and wastewater sector are chlorine and hydrogen sulphide, whereas methane is a flammable gas. CAMEO software is used in this research to predict the region that toxic gas release impacts. This research deals with a sewage treatment plant ERP and control measures for methane and chlorine gases. The affected area of hazard will depend upon the weather conditions and the time of the accident. Comparing two different seasons, the impacted distance is more significant in summer than in winter. It is observed that the night and early morning is more dangerous than the afternoon and evening as it shows the larger impacted distance.

Prevalence of the Acute Respiratory Infections and Associated Factors in the Rural Areas and Urban Slum Areas of Western Maharashtra, India: A Community-Based Cross-Sectional Study.

Acute respiratory infections (ARIs) continue to be the most important cause of morbidity and mortality among under-five children. Some demographic and environmental factors are associated with ARIs among under-five children. This study was conducted with the objective to estimate the prevalence of ARIs among under-five children in the rural areas and densely populated urban slum areas in Maharashtra, India and to assess the association of the selected sociodemographic and household environmental factors with ARI. This study was conducted in 16 selected clusters from the rural areas and densely populated urban slum areas of the two districts in Maharashtra, India. Structured and validated proforma was used for collecting the data on the sociodemographic and household environmental risk factors. A total of 3,671 under-five children were surveyed. The prevalence of ARIs for the preceding month was 50.4%. It was higher among the children living in the rural areas (54.2%) compared to the children living in the urban areas (46.7%) (p = 0.01). The prevalence of ARIs was reported to be 51.4 and 49.4% in boys and girls, respectively. In the multivariate analysis, the researchers found that living in rural areas (p = 0.01) and parental smoking (p = 0.04) were significantly associated with the ARIs. An intervention such as reducing parental smoking habits at the household level may reduce ARIs.

An Eight-Year Profile of Children with Influenza A(H1N1) in a Large Hospital in India.

As influenza virus A(H1N1) continues to circulate, reports from India have documented mainly respiratory involvement in children. This retrospective chart review of children at a medical college found that from August 2009 to July 2017, 855 children aged 3 months to 15 years had H1N1 influenza of whom 310 (36.3%) were admitted and 29 (9.4% admissions) died. In 2009-12, 76.5% patients presented in August-October but from 2015 to 2017, 89.3% came in January-March. The proportion of under-fives increased from 54.0% in 2009-10 to 77.7% in 2015-17. Among admitted children, 82.6% were under 5 years, 96.1% had respiratory symptoms and 11% had seizures. Six children had encephalopathy of whom four died; two survivors had severe neurological sequelae. Other features included gastroenteritis, otitis media, myositis and hepatitis. Complications included shock (10.7%) and acute respiratory distress syndrome (6.1%). Evidence of bacterial/fungal infection was present in 71 (22.9%). Oxygen was required by 123 children (39.7%), high-dependency/intensive care by 47 (15.2%), 17 (5.5%) received high-flow oxygen and 29 (9.4%) required mechanical ventilation. There were no significantly increased odds of needing intensive care or of dying in children with underlying diseases or among different age groups but those with underlying central nervous system (CNS) diseases had higher odds of needing high-dependency/intensive care [odds ratio (OR) 2.35, p = 0.046]. Significantly, children with CNS symptoms had nearly seven times higher odds of needing mechanical ventilation (OR 6.85, p < 0.001) and over three times higher odds of dying (OR 3.31, p = 0.009).Lay summaryH1N1 Influenza ("swine flu") emerged as a global pandemic in 2009 and continues to affect children all over the world. This review of records from a medical college hospital in southern India found that 855 children aged 3 months to 15 years came with H1N1 influenza over 8 years from August 2009 to July 2017. In 2009-12, over three-quarters of them presented in the rainy season but from 2015-17, almost 90% came in the winter and spring, suggesting a change in the seasonality of the outbreaks, which could impact the choice of dates for annual influenza vaccination. The proportion under 5 years of age increased from 54% in 2009-10 to 78% in 2015-17, suggesting possible immunity in children exposed to earlier outbreaks. Over a third of the children needed admission of whom almost 40% needed oxygen, one-sixth needed high-dependency/intensive care and 1 in 11 admitted children died, emphasizing the severity of this disease. While most children had respiratory symptoms, all organs of the body were affected; 11% of those admitted had seizures and 6 had encephalitis. Children admitted with central nervous system symptoms had an almost 7-fold higher risk of needing high-dependency/intensive care and an over 3-fold higher risk of dying.

Automating water quality analysis using ML and auto ML techniques.

Generation of unprocessed effluents, municipal refuse, factory wastes, junking of compostable and non-compostable effluents has hugely contaminated nature-provided water bodies like rivers, lakes and ponds. Therefore, there is a necessity to look into the water standards before the usage. This is a problem that can greatly benefit from Artificial Intelligence (AI). Traditional methods require human inspection and is time consuming. Automatic Machine Learning (AutoML) facilities supply machine learning with push of a button, or, on a minimum level, ensure to retain algorithm execution, data pipelines, and code, generally, are kept from sight and are anticipated to be the stepping stone for normalising AI. However, it is still a field under research. This work aims to recognize the areas where an AutoML system falls short or outperforms a traditional expert system built by data scientists. Keeping this as the motive, this work dives into the Machine Learning (ML) algorithms for comparing AutoML and an expert architecture built by the authors for Water Quality Assessment to evaluate the Water Quality Index, which gives the general water quality, and the Water Quality Class, a term classified on the basis of the Water Quality Index. The results prove that the accuracy of AutoML and TPOT was 1.4 % higher than conventional ML techniques for binary class water data. For Multi class water data, AutoML was 0.5 % higher and TPOT was 0.6% higher than conventional ML techniques.