The rational prescription of dermatological medications is critical in reducing treatment costs and minimizing adverse drug reactions (ADRs). With rising polypharmacy and irrational drug use in dermatology, Structured pharmacovigilance is very important for keeping an eye on drug-related hazards and prescribing trends, especially in emerging nations like India. The aim of this study was to analyze prescribing trends and identify the spectrum of cutaneous adverse drug reactions (cADRs) experienced by dermatology patients receiving treatment at a tertiary care hospital, evaluating their rationale in accordance with WHO prescribing indicators and pharmacovigilance metrics. An observational study was carried out on 979 dermatology outpatients over three months at RUHS CMS, Jaipur. Data were collected using WHO core drug-use indicators, and ADRs were monitored actively via a departmental pharmacovigilance program. Descriptive and inferential statistical analysis were utilized. Collectively, 2,848 drugs were prescribed across 979 encounters, with a mean of 2.75 drugs per prescription. Oral route was predominant (57.3%), with tablets (44.5%) as the most common dosage form. Antifungals (24.8%) and antihistamines (23.6%) were the most frequently prescribed. Itraconazole and levocetirizine were the leading agents. cADRs were reported in 2.3% of patients; steroid-induced acne and photodermatitis were common. All prescriptions adhered to generic naming (100%) and 93.6% Includes medications from the list of necessary drugs. This study underscores generally rational prescribing practices but highlights the need for enhanced ADR monitoring. Dermatology remains a high-risk specialty for cADRs, necessitating continuous pharmacovigilance, education of prescribers, and patient counseling to mitigate preventable drug harms. Keywords: Dermatology, Rational Drug Use, WHO Prescribing Indicators, Adverse Drug Reactions, Pharmacovigilance

To evaluate the diagnostic and prognostic accuracy of nCD64, mHLA-DR and sepsis Index (SI), ratio of nCD64 and mHLA-DR, in patients with sepsis. Prospective observational study was undertaken among 50 cases diagnosed with sepsis, 25 non-septic patients, and 25 healthy individuals as controls. Participants underwent flowcytometric estimation of nCD64 and mHLA-DR on the day of admission. The sepsis cohort had significantly higher nCD64 and lower mHLA-DR expression than both control groups (p-value: < 0.001). The sensitivity and specificity of nCD64 ABC (Antibodies Bound per Cell) for diagnosis of sepsis with a cut-off of 1152.16 was 94

Background: Type 2 Diabetes Mellitus imposes a considerable strain on the Indian healthcare system, with rising prevalence, early age of onset, and complex pharmacological needs. Evaluating drug utilization patterns is critical to ensuring rational prescribing, improving glycemic control, and aligning with evidence-based treatment guidelines. Objective: To analyze the prescribing patterns of antidiabetic medications in patients with T2DM at a tertiary care facility in Central India, with emphasis on drug used, combination therapy, and demographic data. Material and Methods: A prospective exploratory study was performed from January 2022 to December 2024 at Index Medical College, Indore. A total of 408 T2DM patients (aged ≥20 years) attending the OPD were enrolled. Demographic data, comorbidities, and medication prescriptions were gathered via a structured case report form. Descriptive and inferential statistical analyses were performed using SPSS v30. A p-value < 0.05 was regarded as statistically significant. Results: The mean chronological age of the study population was 45.74 ± 10.61years, with a significant male predominance (56.61%). The predominant age group was 41-50 years, comprising 39.95%. Metformin was the most commonly prescribed medication (40.75%), succeeded by glimepiride (21.66%), sitagliptin (10.47%), and dapagliflozin (7.17%). Combination therapy was more prevalent (60.54%) compared to monotherapy (39.46%), with dual therapy (48.04%) being significantly more common than triple therapy (12.5%). The most prescribed fixed-dose combination was metformin with glimepiride (19.12%). The Body Mass Index (BMI) and Glycated hemoglobin (HbA1c) values exhibited a positive connection (r = 0.997). indicating a relationship between obesity and poor glycemic control. Conclusion: This study highlights a strong preference for metformin-based combination regimens, reflecting adherence to standard treatment protocols. The high prevalence of polypharmacy and significant associations with comorbidities underscore the need for periodic prescription audits and individualized therapy to ensure rational drug use and optimize patient outcomes.

Background: There are limited randomised control trials (RCTs) on peripheral arthritis in spondyloarthritis (SpA) and none on oral glucocorticoids (GCs). This study aimed to evaluate the efficacy and safety of oral GCs in treating peripheral arthritis associated with SpA. Methods: This double-blind randomised controlled trial included patients of SpA (non-psoriatic) with active peripheral arthritis (TJC ≥ 2 and SJC ≥ 2) despite NSAIDs and randomised them to receive either oral GC or placebo for 24 weeks. Conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs) and intra-articular and/or intramuscular GCs were allowed in both groups. The primary endpoint was ACR50 at 24 weeks and the secondary endpoint included ACR70 and the Peripheral SpA Response Criterion (PSpARC). Analysis was intention-to-treat (ITT) with non-response imputation. Results: Ninety-four patients (76% male), with a mean age of 31.9 years, with a mean tender joint and swollen joint count of 10 and 3.6, were included and 47 were randomised to receive oral GC and 47 to placebo. Almost all (96%) were receiving csDMARDs by the end of the study. At 24 weeks, there was no significant difference in ACR50 in the GC group compared to placebo (+12.8%; 95% CI, −7.2, 32.7%; P = .213). Secondary endpoints were also not significantly different, including ACR70 (+14.9%, CI −3.2, 32.9%, P = .111) and PSpARC (+10.6%, CI −6.1, 27.3%, P = .216) at 24 weeks. GC was associated with higher adverse effects, specifically, hyperglycaemia. Conclusions: This study did not find oral GCs to be significantly efficacious in peripheral arthritis of SpA patients. However, there was a trend to improvement with GC and it may have missed a significant difference due to the small sample size included. Oral GC was associated with higher adverse effects. Trial registration: Clinical Trial Registry of India (CTRI/2021/08/035887).

Medical image segmentation is a critical task in medical image analysis, and clustering algorithms can be utilized to achieve this goal. This research work focuses on the segmentation of neuro disorder magnetic resonance images using Otsu, K-means, and FCM coupled with the firefly optimization algorithm. Otsu is a classical thresholding algorithm that relies on a single threshold value to segment the images. K-Means is one of the simplest and most widely used clustering algorithms. It aims to partition data into K clusters, where each data point belongs to the cluster with the nearest mean. Fuzzy C-Means is an extension of K-Means, allowing data points to belong to multiple clusters with varying degrees of membership. In medical image segmentation, FCM was used to classify pixels or voxels into different tissue classes with soft boundaries, accounting for partial volume effects. Firefly Optimization helps in improving the convergence speed of the FCM algorithm. Firefly optimization is good at exploring the solution space and finding global optima. The combination of FCM and Firefly Optimization leads to more accurate clustering results. The performance evaluation was done by Renyi entropy and Shannon entropy, FCM coupled with the firefly optimization was found to exhibit superior results when compared with the Otsu and K-means clustering algorithms.

Blockchain technology has emerged as a formidable force ready to transform the pharmaceutical business. This study investigated the integration of smart contracts and decentralised apps as potential future possibilities, emphasising their ability to automate crucial operations and strengthen pharmaceutical product integrity, based on the recently published articles in PubMed between 2015 to 2023 with "pharmacology" and "blockchain" as search keywords. Recent study backed up the idea that blockchain can improve openness, security and efficiency in the industry. According to research, it has the ability to speed up regulatory approvals while also considerably reducing the risk of counterfeit medications penetrating the supply chain. Furthermore, the ability of blockchain to disrupt existing intermediaries and enable disintermediation may result in a more streamlined and efficient industry. While there are implementation obstacles, the benefits of this technology in medicines are significant. Embracing blockchain promises a future of increased security, transparency and patient-centric-ity, ultimately changing healthcare. This article explored blockchain application in the pharmaceutical sector with innovations like Medledger and chaincodes, addressing drug tracing and supply chain security. It presents a structure for a private network using Hyperledger Fabric, showcasing blockchain's potential to enhance transparency, security and efficiency beyond traditional areas.

Hypertrophic obstructive cardiomyopathy (HCM) is the most common heterogeneous genetic cardiovascular disorder. Its pathophysiology involves left ventricular hypertrophy, increased fibrosis, hypercontractility, and reduced compliance. The symptomatic obstructive HCM presents as dyspnoea, syncope, chest pain, palpitations, arrhythmias, or sudden death, usually after provocative manoeuvres like exercise. Until April 2022, treatment options were disease non-specific like Beta blockers, Cardio-selective Calcium Channel Blockers, Dipyridamole, and Ranolazine. Mavacamten is a first-in-class, FDA-approved drug molecule for HCM. It works by selective and reversible inhibition of the cardiac myosin ATPase thereby decreasing the formation of actin- myosin cross- bridges in systole and diastole. The excessive actin-myosin cross-bridging is the hallmark of disease and is responsible for the compromised functioning of the heart in both the systole and diastole phase due to left ventricular outflow tract (LVOT) obstruction and increased ventricular filling pressure respectively. Mavacamten acts by producing super-relaxed state of heart which is then translated into decreased LVOT obstruction and improved cardiac filling pressures thereby improving the functional capacity and symptoms in patients with New York Heart Association (NYHA) stage II, and III symptomatic or obstructive heart failure. Mavacamten is administered orally 2.5-15 mg per day with titration guided by lab investigations and clinical symptoms. Its bioavailability is 85%, undergoes metabolism by CYP2C19 and CYP3A4 and is excreted mainly in urine. It is also an enzyme inducer and shows considerable drug interactions. Common adverse effects are dizziness and syncope. Sometimes the drug may worsen heart failure or completely block ventricular function. Mavacamten has raised hopes for the possibility of managing this potentially lethal intrigue disorder with medicines alone.

Education is the most important factor that profoundly advances human development. It promotes the growth of informed, useful citizens and gives opportunities to the socially and economically marginalised groups in society. Many empirical research by social scientists has shown that education is the most essential component in eliminating poverty because it produces a workforce that can participate in an increasingly competitive and global market. Research has shown a robust association between education and the advancement of a country. It is imperative that people in developing countries have access to basic services including health care, education, and other essentials. The remarkable expansion of the USA and other western countries in almost every aspect of life is well recognised to have been significantly impacted by high literacy rates. Likewise, countries such as Japan, Korea, Singapore, Malaysia, and Thailand had almost universal literacy rates prior to joining the exclusive group of industrialised nations.

Edge detection is a vital aspect of medical image processing, playing a key role in delineating borders and contours within images. This capability is instrumental for various applications, including segmentation, feature extraction, and diagnostic procedures in the realm of medical imaging. COVID-19 is a deadly disease affecting people in most of countries in the world. COVID-19 is due to the coronavirus which belongs to the family of RNA viruses and causes various symptoms such as pneumonia, fever, breathing difficulty, and lung infection. ROI extraction plays a vital role in disease diagnosis and therapeutic treatment. CT scans can help detect abnormalities in the lungs that are characteristic of COVID-19, such as ground-glass opacities and consolidation. This research work proposes an Intuitionistic fuzzy (IF) edge detector for the segmentation of COVID-19 CT images. Intuitionistic fuzzy sets go beyond conventional fuzzy sets by incorporating an additional parameter, referred to as the hesitation degree or non-membership degree. This extra parameter enhances the ability to represent uncertainty more intricately in expressing the degree to which an element may or may not belong to a set. The IF edge detector generates proficient results, when compared with the traditional edge detection algorithms and is validated in terms of performance metrics for benchmark images. Intuitionistic fuzzy edge detection has been shown to be effective in handling uncertainty and imprecision in edge detection.

Secure image communication is becoming more and more important owing to stealing and content manipulation. To protect the confidential image from unauthorized access, encryption algorithms are considered. Due to the unique properties of chaotic systems, the admired encryption algorithm used is the chaos-based encryption algorithm. It deals with a 2D chaos-based encryption algorithm known as Two-Dimensional Tent Cascade Logistic Map (2DTCLM). Cascading, logistic map and tent map generate 2D-TCLM, which is applied on permutation and substitution stages to obtain an encrypted image from the original image. Simulation results show the effect of the proposed algorithm on DICOM images to obtain their corresponding encrypted and decrypted images to know the accuracy of transmission between end users. Performance metrics such as correlation, covariance, and histogram variance are analyzed to show that the transmission is possible in proficient mode.

The semiconductor material InP plays a key role in optoelectronic devices, high-speed devices, and fiber optic communications systems. The major problems with these materials are the high lattice mismatch and variance in thermal expansion coefficient between InP and Si. This mismatch produces high dislocation density at the interface and the propagation of the threading dislocations away from the interface into the device layer is a major concern in optoelectronic applications. Image processing algorithms play a pivotal role in the medical field, archaeology, and remote sensing. This work proposes an image processing method to analyze the SEM images of the InP heteroepitaxy layer to determine the etch pits to confirm whether the substrate is suitable for optoelectronic applications. In this work, a variant of an anisotropic diffusion filter for noise reduction on SEM images and Fuzzy C means clustering method for image segmentation was employed for analysis.

The increasing reliance on teleradiology for remote medical image transmission necessitates robust security measures to safeguard patient information. This paper introduces a novel approach to secure medical images using 3D hyperchaotic-based encryption techniques. The encryption process involves the transformation of medical images into hyperchaotic attractors, making them resistant to conventional attacks. The inherent complexity of the hyperchaotic system ensures a high level of security, and the use of three-dimensional transformations enhances the encryption strength, making it suitable for safeguarding volumetric medical imaging data. The proposed method harnesses the intricate dynamics of hyperchaotic systems for enhancing the integrity and confidentiality of sensitive medical data during transmission and storage. Performance metrics such as entropy, correlation coefficients, NPCR, and UACI are evaluated. The results demonstrate that the 3D hyperchaotic-based encryption method provides a secure and efficient solution for protecting medical images in teleradiology environments. The 3D hyperchaotic-based encryption method offers a promising solution for ensuring the integrity and confidentiality of sensitive medical data, addressing the growing concern of unauthorized access and potential breaches in telemedicine systems.

Recurrent, intense headaches that are accompanied by a number of other symptoms are the hallmark of the complicated neurological illness known as Migraine. This research focuses on finding a therapeutic relationship between Migraine and Music. Music therapy, a specialised field that employs music to address physical, emotional, cognitive, and social needs of individuals, has gained traction in diverse medical settings. We have taken in account several musical characteristics like duration of listening, category of music, effectiveness of music in improving the migraine associated pain, gender wise differences in music listening habits, and many more. Between August 31, 2023, and October 6, 2023, a Pan-India cross-sectional analysis was carried out across several medical colleges in India. A self-administered questionnaire was utilised to collect data using web-based links and statistical analysis was made using appropriate tools. All participants were 18 years old or older, moreover all of them were enrolled in MBBS colleges in India. A total of 384 students participated in the study (170 Females and 214 Males). With the help of Headache Screening Questionnaire (HSQ) 8.41% males and 13.52% females are suspected migraine patients. Furthermore, the type of music preferred by subjects and the duration of music listening was taken into account. Then, the Chi-squared value was obtained as 4.40649125 and with the help of the same, P-value was obtained to be 0.110449. The p-value < 0.05, indicating that the result is not statistically significant. While this study did not find a robust relationship between music and migraine pain relief, future research with more advanced tools and a larger sample size could potentially uncover a significant association. The current findings suggest that music may play a modest role in the therapeutic management of migraine pain. However, further exploration is warranted, as music therapy holds promise as a fascinating complementary approach that could aid in migraine treatment with minimal or no medication. Delving deeper into the psychological and neurological mechanisms underlying music's perceived efficacy in migraine management may elucidate its true therapeutic potential. Keywords:­ Migraine, Music Therapy, Migraine Treatment, Music, Migraine

This research work proposes a computer-aided algorithm for the conversion of building plans into a graph for the navigation of robots. Rescuing people from burning buildings manually is a tedious process. Robots play a pivotal role in industrial automation and the deployment of robots in rescue operations can save many people. Indoor navigation for robots is a challenging task since every building has a unique structure. A routing graph is inevitable to find the path in a building quickly for the navigation of robots to perform the rescue operation. The automatic extraction of the routing graph from the image of the floor plan is offered in this research work. The floor plan images are acquired and converted into a raster image. Then, by using the predefined kernels, the white pixels are eroded for obtaining the routing path of common walkways through corridors and rooms. The Extended Conditional Erosion Algorithm is used for the extraction of the routing graph from the floor plan images. The resultant graph as output aids the navigation of the robot.

Steganography is the practice of hiding one piece of information within another, and in the context of medical images, it involves concealing sensitive patient data within the images for secure transmission and storage. Bioinspired algorithms play a significant role in medical image steganography for healthcare applications. They may be used to design complex encryption and embedding schemes that are difficult for unauthorized users to break. These algorithms mimic the robustness of biological systems against various attacks. They can be employed to find the best locations within the medical images to hide data while minimizing the impact on image quality. Bioinspired algorithms, such as genetic algorithms or swarm intelligence, are also used to create adaptive systems that optimize the embedding process according to specific criteria. Bioinspired algorithms may be designed to ensure that the hidden data remains robust even when the images are subjected to compression or noise reduction processes. This enhances the reliability of the steganography technique in practical healthcare scenarios. Bioinspired algorithms can be optimized for speed and efficiency, enabling rapid embedding and extraction of data from medical images without significant delays. In this chapter, the authors do a comparative study of the various bioinspired algorithms for medical image steganography.

Coffee intake is a popular and widespread habit worldwide, with many individuals relying on it for its stimulating effects on cognitive function and mood. However, coffee's neurologic and psychiatric effects have been the subject of debate among researchers and health-care professionals. This review essay aims to synthesize the existing literature on the topic to understand better the relationship between coffee intake and neurologic and psychiatric outcomes. A systematic search of the PubMed database was conducted to identify relevant research articles published between 2005 and 2022. The search terms included “coffee,” “neurologic,” “psychiatric,” “cognitive,” “mood,” and “depression.” Studies were included if they examined the effects of coffee intake on neurologic or psychiatric outcomes, were published in English, and were conducted on human participants. A total of 29 studies were included in the final review. The studies reviewed in this essay provide evidence for coffee intake's neurologic and psychiatric effects. Caffeine, the essential psychoactive compound in coffee, is known to have stimulating effects on the central nervous system. It has also been shown to improve cognitive function and attention by blocking adenosine receptors in the brain, leading to increased activity in the prefrontal cortex. Several studies have demonstrated that moderate coffee intake can enhance cognitive function, particularly in older adults. In addition, the consumption of coffee has been implicated with a reduced risk of depression, as well as a lower risk of developing Parkinson's disease. In conclusion, coffee intake positively affects cognitive functioning and mood, particularly in moderation. However, further research is required to understand the mechanisms behind these effects and determine the optimal coffee intake for neurologic and psychiatric benefits. In addition, future research should explore the potential adverse effects of excessive coffee intake, such as anxiety and sleep disturbances, to inform safe coffee intake recommendations for individuals. Overall, this review essay provides valuable insight into the neurologic and psychiatric effects of the consumption of coffee and highlights the need for further research in this field.