3D-Bioprinting and AI-empowered Anatomical Structure Designing: A Review.

BACKGROUND: The recent advancements and detailed studies in the field of 3D bioprinting have made it a promising avenue in the field of organ shortage, where many patients die awaiting transplantation. The main challenges bioprinting faces are precision during printing, vascularization, and cell proliferation. Additionally, overcoming these shortcomings requires experts from engineering, medicine, physics, etc., and if accomplished, it will significantly benefit humankind. OBJECTIVE: This paper covers the general roadmap of the bioprinting process, different kinds of bioinks, and available bioprinters. The paper also includes designing the anatomical structure, which is the first phase of the bioprinting process, and how AI has facilitated this entire process of 3D printing in healthcare and associated applications like medical modelling and disease modelling. METHODS: The process of 3D bioprinting involves meticulous structure designing of the anatomical structure under study, which forms the base of the entire bioprinting process. One of the significant applications of 3D printing in healthcare is Medical Modelling and Disease Modelling, which requires the detection of disease in anatomy and its delineation from the rest of anatomy for meticulous creation of ROI using sophisticated segmentation software(s) for the construction of 3D models of diseased anatomy and healthy anatomical surroundings. CONCLUSION: The study concluded that bioprinting is the future of the worldwide organ transplantation crisis. Anatomical accuracy is an important aspect that must be considered while producing 3D models. The reproduction of patient-specific 3D models requires human rights and ethics approval under four principles of ethics in healthcare: autonomy, non-maleficence, beneficence, and justice.

Effect of age of tobacco initiation and number of failed quit attempts on maintenance of tobacco abstinence.

BACKGROUND: The decision to make a quit attempt is the first step toward the tobacco cessation process. It is well established in the literature that if someone does not take tobacco till the age of 21 years then his chances of remaining tobacco-free for life are higher than his counterparts who start tobacco at early developmental ages. METHODOLOGY AND TOOLS: The present study was conducted among 400 university undergraduate students. A cross-sectional survey design was used, multi-stage sampling was done, and four colleges were selected via random sampling. The motivation to quit tobacco, tobacco craving, and maintenance of tobacco abstinence was assessed via contemplation ladder, tobacco craving questionnaire Short Form, and smoking abstinence questionnaire. To validate subjective data, a urine cotinine test was performed. RESULTS: The age of tobacco initiation significantly impacts intentions to quit tobacco and tobacco craving levels. The number of unsuccessful quit attempts was also significantly associated with the maintenance of tobacco abstinence. The failed quit attempts play a vital role in altering tobacco cravings and make the withdrawals more complicated to handle for recent tobacco quitters.

Design and Development of Hypertuned Deep learning Frameworks for Detection and Severity Grading of Brain Tumor using Medical Brain MR images.

BACKGROUND: Brain tumor is a grave illness causing worldwide fatalities. The current detection methods for brain tumors are manual, invasive, and rely on histopathological analysis. Determining the type of brain tumor after its detection relies on biopsy measures and involves human subjectivity. The use of automated CAD techniques for brain tumor detection and classification can overcome these drawbacks. OBJECTIVE: The paper aims to create two deep learning-based CAD frameworks for automatic detection and severity grading of brain tumors - the first model for brain tumor detection in brain MR images and model 2 for the classification of tumors into three types: Glioma, Meningioma, and Pituitary based on severity grading. METHODS: The novelty of the research work includes the architectural design of deep learning frameworks for detection and classification of brain tumor using brain MR images. The hyperparameter tuning of the proposed models is done to achieve the optimal parameters that result in maximizing the models' performance and minimizing losses. RESULTS: The proposed CNN models outperform the existing state of the art models in terms of accuracy and complexity of the models. The proposed model developed for detection of brain tumors achieved an accuracy of 98.56% and CNN Model developed for severity grading of brain tumor achieved an accuracy of 92.36% on BraTs dataset. CONCLUSION: The proposed models have an edge over the existing CNN models in terms of less complexity of the structure and appreciable accuracy with low training and test errors. The proposed CNN Models can be employed for clinical diagnostic purposes to aid the medical fraternity in validating their initial screening for brain tumor detection and its multi-classification.

Motivation to quit tobacco; Impact of different types of Anti-tobacco state-sponsored media propaganda messages.

Introduction: Antitobacco media messages can easily reach the mass and play a very positive and significant role in changing the motivational stages among recent quitters. Motivation is the key to changing human behaviour. Motivation can be intrinsic and extrinsic. To modify tobacco-related behaviour, one must have an inherent motivation to quit tobacco. However, the outside factors, for example, protobacco advertisements, antitobacco advertisements, peer pressure, celebrity influence, and family members' influence cannot be ignored. Method: A total of 400 recent tobacco quitters were enrolled from four colleges via a multistage sampling method. Time series research design was used for data collection at three time periods 0, 1, and 3 months. Study participants were divided into four groups: 1) personal testimony group, 2) health warning group, 3) celebrity-influenced public service announcements, and 4) natural exposure group. Media messages containing antitobacco video clippings and pictures were delivered to the participants via phone thrice a week, as per the groups assigned. All four groups were assessed for the motivational stage via contemplation ladder at 0, 1, and 3 months of intervals. Results: Antitobacco personal testimonial media messages are most effective in enhancing the motivation to quit tobacco, followed by the antitobacco health warning messages, which are also proven to be effective in maintaining high motivation levels to remain abstinent from smoking. However, public service announcements are ineffective in keeping the motivation to quit tobacco at higher smoking. Conclusion: Overall, the antitobacco state-sponsored media messages, personal testimonials, and health warnings about tobacco products effectively maintain and enhance motivation to quit tobacco.

Bioactive Loaded Novel Nano-Formulations for Targeted Drug Delivery and Their Therapeutic Potential.

Plant-based medicines have received a lot of attention in recent years. Such medicines have been employed to treat medical conditions since ancient times, and in those times only the observed symptoms were used to determine dose accuracy, dose efficacy, and therapy. Rather than novel formulations, the current research work on plant-based medicines has mostly concentrated on medicinal active phytoconstituents. In the past recent decades, however, researchers have made significant progress in developing "new drug delivery systems" (NDDS) to enhance therapeutic efficacy and reduce unwanted effects of bioactive compounds. Nanocapsules, polymer micelles, liposomes, nanogels, phytosomes, nano-emulsions, transferosomes, microspheres, ethosomes, injectable hydrogels, polymeric nanoparticles, dendrimers, and other innovative therapeutic formulations have all been created using bioactive compounds and plant extracts. The novel formulations can improve solubility, therapeutic efficacy, bioavailability, stability, tissue distribution, protection from physical and chemical damage, and prolonged and targeted administration, to name a few. The current study summarizes existing research and the development of new formulations, with a focus on herbal bioactive components.

Vanishing bone disease: An enigma.

Vanishing bone disease is a rare clinical entity with unknown etiology. This disease affects individual irrespective of age or sex. Various names have been used in the literature to describe this condition such as Gorham's disease, phantom bone disease, massive osteolysis, disappearing bone disease and acute spontaneous absorption of bone. The pathogenesis is unknown and the treatment still remains controversial. Considering the rarity of the disease, we report here an interesting and unique case of massive osteolysis of the lower jaw that affected the mandibular basal and alveolar bone. The diagnoses lead on the association of clinical, radiological and histological features.

Deep Learning-Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model.

The diabetic retinopathy accounts in the deterioration of retinal blood vessels leading to a serious compilation affecting the eyes. The automated DR diagnosis frameworks are critically important for the early identification and detection of these eye-related problems, helping the ophthalmic experts in providing the second opinion for effectual treatment. The deep learning techniques have evolved as an improvement over the conventional approaches, which are dependent on the handcrafted feature extraction. To address the issue of proficient DR discrimination, the authors have proposed a quadrant ensemble automated DR grading approach by implementing InceptionResnet-V2 deep neural network framework. The presented model incorporates histogram equalization, optical disc localization, and quadrant cropping along with the data augmentation step for improving the network performance. A superior accuracy performance of 93.33% is observed for the proposed framework, and a significant reduction of 0.325 is noticed in the cross-entropy loss function for MESSIDOR benchmark dataset; however, its validation utilizing the latest IDRiD dataset establishes its generalization ability. The accuracy improvement of 13.58% is observed when the proposed QEIRV-2 model is compared with the classical Inception-V3 CNN model. To justify the viability of the proposed framework, its performance is compared with the existing state-of-the-art approaches and 25.23% of accuracy improvement is observed.

Concerns regarding Indian prosthetic eye wearers.

CLINICAL RELEVANCE: Loss of an eye has a negative impact on the psychological health of a person. BACKGROUND: The aims are to: (a) identify the concerns of experienced prosthetic eye wearers in representative areas of India; (b) investigate how they change over time; (c) study how some demographic characteristics influence concerns; and (d) compare prosthetic eye concerns in India versus Germany and New Zealand. METHODS: This observational multi-centre study was conducted in the cities of Mumbai, Bengaluru and Delhi. Subjects aged from 18 to 65-years, with experience of at least two years of wearing a prosthetic eye fabricated from polymethyl methacrylate material, completed a validated questionnaire over the telephone. The questionnaire captured demographic data and general and prosthetic eye concerns at the time of eye loss and at the present time. RESULTS: At the time of eye loss, the five main concerns of participants were: health of the remaining eye; change in appearance; comfort; colour of the prosthesis; and movement of the prosthesis. However, after at least two years these concerns were significantly reduced (p < 0.05). The main present-day concern was watering, crusting and discharge, which was experienced by 81.1-per cent of the participants, 55.5-per cent of them on a daily basis. Compared with other demographic groups, blue-collar workers had higher odds of being concerned about the health of the remaining eye and least odds about prosthesis motility. Homemakers had higher odds of being concerned about wearing comfort and students had higher odds of being concerned about wearing comfort, appearance and health of the remaining eye. CONCLUSION: Initial concerns significantly reduce after two years. Watering, crusting and discharge are the most important current concerns for anophthalmic participants.

A Block Adaptive Near-Lossless Compression Algorithm for Medical Image Sequences and Diagnostic Quality Assessment.

The near-lossless compression technique has better compression ratio than lossless compression technique while maintaining a maximum error limit for each pixel. It takes the advantage of both the lossy and lossless compression methods providing high compression ratio, which can be used for medical images while preserving diagnostic information. The proposed algorithm uses a resolution and modality independent threshold-based predictor, optimal quantization (q) level, and adaptive block size encoding. The proposed method employs resolution independent gradient edge detector (RIGED) for removing inter-pixel redundancy and block adaptive arithmetic encoding (BAAE) is used after quantization to remove coding redundancy. Quantizer with an optimum q level is used to implement the proposed method for high compression efficiency and for the better quality of the recovered images. The proposed method is implemented on volumetric 8-bit and 16-bit standard medical images and also validated on real time 16-bit-depth images collected from government hospitals. The results show the proposed algorithm yields a high coding performance with BPP of 1.37 and produces high peak signal-to-noise ratio (PSNR) of 51.35 dB for 8-bit-depth image dataset as compared with other near-lossless compression. The average BPP values of 3.411 and 2.609 are obtained by the proposed technique for 16-bit standard medical image dataset and real-time medical dataset respectively with maintained image quality. The improved near-lossless predictive coding technique achieves high compression ratio without losing diagnostic information from the image.

Relationship between Severity of Periodontal Disease and Control of Diabetes (Glycated Hemoglobin) in Patients with Type 1 Diabetes Mellitus.

BACKGROUND: Both diabetes mellitus (DM) and periodontitis are chronic diseases affecting large number of the population worldwide. Changes in human behavior and lifestyle over the last century have resulted in a dramatic increase in the incidence of diabetes in the world. This study was designed to evaluate the relationship between severity of periodontal disease and control of diabetes (glycated hemoglobin [HBA1c]) in patients with Type 1 DM in a hospital based study. MATERIALS AND METHODS: Fifty patients (n = 50) with Type 1 diabetes were enrolled in the study. They were divided into three groups based on the degree of glycemic control by measuring HbA1c levels as: "Good" (HBA1c ≤7) Group A, fair (HBA1c = 7-8) Group B and poor (HBA1c >8) Group C. All enrolled patients underwent detailed history and dental checkup. Evaluation for periodontal disease was done by measuring dental plaque (plaque index), inflammation of gums (gingival index), probing pocket depth (PPD), and clinical attachment level. RESULTS: Type 1 diabetics with poor glycemic control had increased gingival inflammation (P < 0.05), more dental plaque (P < 0.05), increased PPDs (P < 0.05) and attachment loss (P < 0.05) as compared to those with fair and good glycemic control, respectively. CONCLUSION: Severity of periodontal disease increases with poor glycemic control in patients with Type 1 DM.

Effectiveness of a five-step method for teaching clinical skills to students in a dental college in India.

This study conducted at the PDM Dental College and Research Institute, Haryana, India, had the purpose of developing a teaching method based upon a five-step method for teaching clinical skills to students proposed by the American College of Surgeons. This five-step teaching method was used to place fissure sealants as an initial procedure by dental students in clinics. The sealant retention was used as an objective evaluation of the skill learnt by the students. The sealant retention was 92 percent at six- and twelve-month evaluations and 90 percent at the eighteen-month evaluation. These results indicate that simple methods can be devised for teaching clinical skills and achieve high success rates in clinical procedures requiring multiple steps.

Evaluation of periodontal disease in dental students.

A cross-sectional study was conducted on dental students at Bahadurgarh, Haryana, for the assessment of periodontal health and possible associated factors. A total of 329 students were examined clinically by periodontists. Students with bleeding on probing and attachment loss ≥ 2 mm were assessed further for extent of bone loss radiographically. The awareness of disease and knowledge of management was also assessed. Sixty-three (19.1%) students showed clinical signs of disease and 21 (6.3%) had cemento-enamel junction (CEJ) to the bone crest distance of ≥2 mm. Proximal carious lesions were observed in nine cases of attachment loss. Mottled enamel with a roughened surface had association with attachment loss. The clinical students showed reasonable knowledge of disease and management. The need to strengthen the diagnostics for early detection of periodontal disease in young adults is suggested.