Revolutionizing dementia detection: Leveraging vision and Swin transformers for early diagnosis.

Dementia, an increasingly prevalent neurological disorder with a projected threefold rise globally by 2050, necessitates early detection for effective management. The risk notably increases after age 65. Dementia leads to a progressive decline in cognitive functions, affecting memory, reasoning, and problem-solving abilities. This decline can impact the individual's ability to perform daily tasks and make decisions, underscoring the crucial importance of timely identification. With the advent of technologies like computer vision and deep learning, the prospect of early detection becomes even more promising. Employing sophisticated algorithms on imaging data, such as positron emission tomography scans, facilitates the recognition of subtle structural brain changes, enabling diagnosis at an earlier stage for potentially more effective interventions. In an experimental study, the Swin transformer algorithm demonstrated superior overall accuracy compared to the vision transformer and convolutional neural network, emphasizing its efficiency. Detecting dementia early is essential for proactive management, personalized care, and implementing preventive measures, ultimately enhancing outcomes for individuals and lessening the overall burden on healthcare systems.

Mental Health Among Bus Drivers and Conductors: A Cross-Sectional Study From Karaikal, South India.

Background Health hazards faced by bus drivers and conductors due to their stressful working conditions can vary greatly depending on the country and specific circumstances. In some regions, stringent regulations mitigate the risks, while in others, lack of enforcement exacerbates the situation. The common mental health issues faced by bus drivers and conductors are anxiety and depression. Therefore, this study was planned to determine the prevalence of anxiety and depression among bus drivers and conductors and identify the risk factors associated with depression and anxiety. Methodology A cross-sectional study was conducted among the bus drivers and conductors working in government and private transport in Karaikal, Puducherry, South India. After institutional ethics committee clearance, 450 male transport workers with a mean age of 42.6 ± 8.56 (SD) years were interviewed to assess their mental status according to the Hospital Anxiety and Depression Scale. Bivariate and multivariate analyses were employed using SPSS software version 20 (IBM Corp., Armonk, NY, USA) to ascertain the association between dependent (anxiety/depression) and independent variables. Results About 16% and 13% of bus drivers and conductors were suffering from anxiety and depression, respectively. Age, education, marital status, bus route type, years of experience, and depression were significantly associated with anxiety. Whereas type of employment, years of experience, and anxiety were significantly associated with depression (p < 0.05). Conclusions Anxiety and depression in drivers and conductors constitute a major public health problem. Proper job rotation, enabling good ergonomics, and social support are some recommendations to promote mental health among transport workers.

Effect of skin phototype on quantitative nailfold capillaroscopy.

Objectives: To determine the impact of Fitzpatrick scale-based skin phototype on visualization of capillary density using nailfold capillaroscopy in healthy Indian adults. Methods: In this cross-sectional study, healthy adults were examined for nailfold capillaroscopy findings utilizing a portable capillary microscope at 800× magnification. Photographs of two contiguous areas measuring 1 mm2 each of the distal row of capillaries were captured. Images were captured from the central area of all fingers except thumb in both hands. Capillary density and morphology of nailfold capillaroscopies were assessed by two blinded assessors. The nailfold capillaroscopy parameters were compared between the Standard Fitzpatrick scale-based skin phototypes. Results: A total of 118 healthy adults were enrolled in the study. Type III, IV, V, and VI skin phototypes were seen in 27 (22.90%), 32 (27.19%), 29 (24.58%), and 30 (25.42%) participants, respectively. All participants (100%) had normal nailfold capillaroscopy morphology and architecture. Zero capillaries were visible in 11 fingers among 5 patients (4.24%) and all of them had Type VI phototype. The median capillary density per mm was 5.19 (interquartile range = 4.37-6.75) with 90 (76.27%) participants having less than seven capillaries. The median average capillary density was significantly different (p-value < 0.0001) across Type III (8.13, interquartile range = 6.44-8.88), Type IV (5.67, interquartile range = 4.41-6.98), Type V (4.94, interquartile range = 4.19-5.38), and Type VI (4.53, interquartile range = 3.72-4.91) phototypes (p < 0.05). Conclusion: The number of capillaries visualized during nailfold capillaroscopy decreases as the skin pigmentation increases. There is a need to redefine the nailfold capillaroscopy density and avascularity by taking skin phototype as one of the determinants before labeling a nailfold capillaroscopy finding with less visualized capillaries as abnormal.

A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts.

Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for digitalization and transcription, recognizing the cursive characters found in palm leaf manuscripts remains an open problem. In this research, a unique Convolutional Neural Network (CNN) technique is utilized to train the characteristics of the palm leaf characters. By this training, CNN can classify the palm leaf characters significantly on training phase. Initially, a preprocessing technique to remove noise in the input image is done through morphological operations. Text Line Slicing segmentation scheme is used to segment the palm leaf characters. In feature processing, there are some major steps used in this study, which include text line spacing, spacing without obstacle, and spacing with an obstacle. Finally, the extracted cursive characters are given as input to the CNN technique for final classification. The experiments are carried out with collected cursive Tamil palm leaf manuscripts to validate the performance of the proposed CNN with existing deep learning techniques in terms of accuracy, precision, recall, etc. The results proved that the proposed network achieved 94% of accuracy, where existing ResNet achieved 88% of accuracy.

Tricomposite gelatin-carboxymethylcellulose-alginate bioink for direct and indirect 3D printing of human knee meniscal scaffold.

The development of technologies that could ease the production of customizable patient-specific tissue engineering constructs having required biomechanical properties and restoring function in damaged tissue is the need of the hour. In this study, we report the optimization of composite, bioactive and biocompatible tripolymeric hydrogel bioink, suitable for both direct and indirect printing of customizable scaffolds for cartilage tissue engineering applications. A customized hierarchical meniscal scaffold was designed using solid works software and developed using a negative mould made of polylactic acid (PLA) filament and by a direct 3D printing process. A composite tripolymeric bioink made of gelatin, carboxymethyl cellulose (CMC) and alginate was optimized and characterized for its printability, structural, bio-mechanical and bio-functional properties. The optimized composite hydrogel bioink was extruded into the negative mould with and without live cells, cross-linked and the replica of meniscus structure was retrieved aseptically. The cellular proliferation, apatite formation, and extracellular matrix secretion from negative printed meniscal scaffold were determined using MTT, live/dead and collagen estimation assays. A significant increase in collagen secretion, cellular proliferation and changes in biomechanical properties was observed in the 3D scaffolds with MG63-osteosarcoma cells indicating its suitability for cartilage tissue engineering.

A comprehensive review on antimicrobial face masks: an emerging weapon in fighting pandemics.

The world has witnessed several incidents of epidemics and pandemics since the beginning of human existence. The gruesome effects of microbial threats create considerable repercussions on the healthcare systems. The continually evolving nature of causative viruses due to mutation or re-assortment sometimes makes existing medicines and vaccines inactive. As a rapid response to such outbreaks, much emphasis has been placed on personal protective equipment (PPE), especially face mask, to prevent infectious diseases from airborne pathogens. Wearing face masks in public reduce disease transmission and creates a sense of community solidarity in collectively fighting the pandemic. However, excessive use of single-use polymer-based face masks can pose a significant challenge to the environment and is increasingly evident in the ongoing COVID-19 pandemic. On the contrary, face masks with inherent antimicrobial properties can help in real-time deactivation of microorganisms enabling multiple-use and reduces secondary infections. Given the advantages, several efforts are made incorporating natural and synthetic antimicrobial agents (AMA) to produce face mask with enhanced safety, and the literature about such efforts are summarised. The review also discusses the literature concerning the current and future market potential and environmental impacts of face masks. Among the AMA tested, metal and metal-oxide based materials are more popular and relatively matured technology. However, the repeated use of such a face mask may pose a danger to the user and environment due to leaching/detachment of nanoparticles. So careful consideration is required to select AMA and their incorporation methods to reduce their leaching and environmental impacts. Also, systematic studies are required to establish short-term and long-term benefits.

OctNET: A Lightweight CNN for Retinal Disease Classification from Optical Coherence Tomography Images.

BACKGROUND AND OBJECTIVE: Retinal diseases are becoming a major health problem in recent years. Their early detection and ensuing treatment are essential to prevent visual damage, as the number of people affected by diabetes is expected to grow exponentially. Retinal diseases progress slowly, without any discernible symptoms. Optical Coherence Tomography (OCT) is a diagnostic tool capable of analyzing and identifying the quantitative discrimination in the disease affected retinal layers with high resolution. This paper proposes a deep neural network-based classifier for the computer-aided classification of Diabetic Macular Edema (DME), drusen, Choroidal NeoVascularization (CNV) from normal OCT images of the retina. METHODS: In the proposed method, we demonstrate the feasibility of classifying and detecting severe retinal pathologies from OCT images using a deep convolutional neural network having six convolutional blocks. The classification results are explained using a gradient-based class activation mapping algorithm. RESULTS: Training and validation of the model are performed on a public dataset of 83,484 images with expert-level disease grading of CNV, DME, and drusen, in addition to normal retinal image. We achieved a precision of 99.69%, recall of 99.69%, and accuracy of 99.69% with only three misclassifications out of 968 test cases. CONCLUSION: In the proposed work, downsampling and weight sharing were introduced to improve the training efficiency and were found to reduce the trainable parameters significantly. The class activation mapping was also performed, and the output image was similar to the retina's actual color OCT images. The proposed network used only 6.9% of learnable parameters compared to the existing ResNet-50 model and yet outperformed it in classification. The proposed work can be potentially employed in real-time applications due to reduced complexity and fewer learnable parameters over other models.

Appropriateness of antibiotic usage for gastrointestinal disorders in a tertiary care hospital.

OBJECTIVE: To assess antibiotic usage in gastrointestinal disorders with respect to appropriateness, pattern of resistance, and incidence of adverse drug reactions (ADRs). METHODOLOGY: Antibiotic prescribing in the gastroenterology department of a tertiary care hospital was evaluated using the Gyssens criteria and also by assessing drug related problems (DRPs) using the Pharmaceutical Care Network Europe V.6.2. A total of 173 patients were studied prospectively by a team of clinical pharmacists. Antibiotic susceptibility was prospectively studied; in addition, retrospective data on culture and sensitivity reports of commonly isolated organisms from 1 October 2012 to 30 September 2014 were collected to determine the resistance pattern in previous years. ADRs were evaluated using the Naranjo scale. RESULTS: Antibiotic therapy was appropriate in 60% of patients and inappropriate in the remaining patients due to incorrect decision, choice, and use. A total of 184 DRPs and 30 ADRs of antibiotics were identified. In the study patients, the most commonly isolated organism was Escherichia coli (27.3%) followed by Klebsiella pneumoniae (16.7%). Both E coli and K pneumoniae exhibited 100% resistance towards cefotaxime. There was an increase in the resistance of E coli and K pneumoniae against various antibiotics tested in 2013-2014 as compared to the previous year. An empirical antibiotic policy was developed which was endorsed by the gastroenterology department. CONCLUSIONS: Although antibiotic therapy was appropriate in the majority of patients, irrational use occurred due to incorrect choice, improper dosage, and improper duration of therapy. E coli and K pneumoniae isolates showed an increase in resistance towards various antibiotics tested.

PCR- RFLP based bacterial diversity analysis of a municipal sewage treatment plant.

Bacterial diversity of sewage soil is an essential study to discover novel bacterial species involved in biodegradation. Restriction Fragment Length Polymorphism is one of the most useful molecular technique for diversity analysis in terms of cost effectiveness and reliability. The present study focuses on bacterial diversity of municipal sewage treatment plant in Chennai, Tamil Nadu, India through metagenomic approach. A 16S r DNA clone library was constructed from metagenomic DNA of sewage soil. 200 clones from the library were subjected to colony PCR and RFLP analysis. Upon RFLP analysis, 16 different Operational Taxonomic Units (OTU's) were obtained and a single clone from each OTU was subjected to sequencing. Phylogenetic analysis of sequences revealed the presence of five different groups of bacteria namely Proteobacteria (56%), Actinobacteria (7%), Firmicutes (5%), Bacteroidetes (17%) and Plancomycetes (7%). Three novel and uncultured groups of bacteria (8%) were also discovered. Most of the organisms identified through this study were reported to be efficient degraders of hydrocarbons, aromatic compounds and heavy metals, thereby promoting biodegradation of polluted environment.

Efficacy and Safety of COX-2 Inhibitors in the Clinical Management of Arthritis: Mini Review.

In the clinical management of arthritis, the choice of nonsteroidal anti inflammatory drug (NSAID) remains confusing and controversial. A common practice on the choice of NSAID in clinical management of arthritis is the risk benefit ratio. The main objective of this review is to addresses the main arguments for the pharmacological and clinical use of COX-2 inhibitors in relation to nonselective NSAIDs for the clinical management of arthritis. This review concluded that, both NSAIDs and COX-2 inhibitors are equally effective and are associated with increased risk of GI, renal, and CV, adverse effects. Complete understanding of the patient's comorbid conditions and concomitant medications, coupled with precise monitoring during the treatment, may help to decrease the threat of adverse effects induced by nonselective NSAIDs and selective COX-2 inhibitors.

Formulation and in vitro-in vivo evaluation of ketoprofen-loaded albumin microspheres for intramuscular administration.

The objective of this work was to prepare and evaluate ketoprofen-loaded albumin microspheres for intramuscular administration. Microspheres were prepared by emulsion cross-linking method using a 2(3) factorial design and the effect of different factors on entrapment efficiency was determined. Microspheres were evaluated for entrapment efficiency, percentage yield, particle size and release behaviour. Selected formulations were then tested by differential scanning calorimetry, X-ray diffraction and scanning electron microscopy. Further they were analysed for residual solvents, syringeability and stability. Microspheres were then sterilized and bioavailability studies were carried out in New Zealand white rabbits. The physical characteristics of microspheres showed that they were suitable for IM administration. The sterilization technique adopted was adequate to maintain sterility. In vivo studies showed increase in C(max), AUC, t(1/2) and MRT (p < 0.05) administered in the form of microspheres. MRT of ketoprofen was almost 3.2-times in the form of microspheres. From these results it was concluded that the developed albumin microspheres of ketoprofen is a potential delivery system for once-a-day intramuscular administration.

Quantitation of serrapeptase in formulations by UV method in the microplate format.

Serrapeptase is an anti-inflammatory, proteolytic enzyme isolated from the microorganism, Serratia sp. HY-6. Very few methods are available for the quantification of serrapeptase. The activity of the enzyme is determined by an ELISA assay, colorimetric method using casein as substrate or by HPLC method. These methods are lengthy, time consuming and require a number of reagents and solvents. Therefore an attempt was made to develop a simple alternative method for regular estimation of drug in formulations. Serrapeptase enzyme was estimated in formulations by using microplate readers which uses the principle of vertical photometry. Further this method was validated and the robustness of this method was checked by estimating the drug in various formulations including liposomes and marketed tablet formulations. A linear relationship between drug concentration and absorbance was observed between 1-4 microg/ml at 230 nm (R(2)=0.9911). The percentage recovery values of the drug in serrapeptase liposomes were found to lie within the standard limit (97-98%) which confirms the method is accurate and free from any positive or negative interference of the excipient. The low value of standard deviation obtained confirms the precision of the method. (+/-0.020 - +/-0.044). The drug content values in marketed tablets values obtained matched the label claim. The proposed microplate UV-method for determination of serrapeptase in formulations is novel, simple, inexpensive, fast, specific and robust. Thus this method could be a better alternative for regular estimation of drug in the various marketed formulations of serrapeptase.

Formulation and evaluation of ketorolac tromethamine-loaded albumin microspheres for potential intramuscular administration.

The objective of this work was to prepare and evaluate ketorolac tromethamine-loaded albumin microspheres using a factorial design. Albumin microspheres were prepared by emulsion cross-linking method. Selected formulations were characterized for their entrapment efficiency, particle size, surface morphology, and release behavior. Analysis of variance (ANOVA) for entrapment efficiency indicated that entrapment efficiency is best fitted to a response surface linear model. From the statistical analysis it was observed that as the drug:polymer (D:P) ratio and volume of glutaraldehyde increased, there was a significant increase in the encapsulation efficiency. Scanning electron microscopy of the microspheres revealed a spherical, nonporous and uniform appearance, with a smooth surface. Based on the entrapment efficiency and physical appearance, 9 formulations were selected for release study. The maximum particle size observed was below 40 microm. The release pattern was biphasic, characterized by an initial burst effect followed by a slow release. All selected microspheres, except those having less polymer proportion (D:P ratio is 1:1), exhibited a prolonged release for almost 24 hours. On comparing r (2) values for Higuchi and Peppas kinetic models, different batches of microspheres showed Fickian, non-Fickian, and diffusion kinetics. The release mechanism was regulated by D:P ratio and amount of cross-linking agent. From the experimental data obtained with respect to particle size and extent of drug release, it could be concluded that the prepared microspheres are useful for once-a-day intramuscular administration of ketorolac tromethamine.