Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain function

Complex & Intelligent Systems, Год журнала: 2024, Номер 10(4), С. 5883 - 5915

Опубликована: Апрель 4, 2024

Abstract Depression is a multifactorial disease with unknown etiology affecting globally. It’s the second most significant reason for infirmity in 2020, about 50 million people worldwide, 80% living developing nations. Recently, surge depression research has been witnessed, resulting multitude of emerging techniques developed prediction, evaluation, detection, classification, localization, and treatment. The main purpose this study to determine volume conducted on different aspects such as genetics, proteins, hormones, oxidative stress, inflammation, mitochondrial dysfunction, associations other mental disorders like anxiety stress using traditional medical intelligence (medical AI). In addition, it also designs comprehensive survey treatment planning, genetic predisposition, along future recommendations. This work designed through methods, including systematic mapping process, literature review, network visualization. we used VOSviewer software some authentic databases Google Scholar, Scopus, PubMed, Web Science data collection, analysis, designing picture study. We analyzed 60 articles related intelligence, 47 from machine learning 513,767 subjects (mean ± SD = 10,931.212 35,624.372) 13 deep 37,917 3159.75 6285.57). Additionally, found that stressors impact brain's cognitive autonomic functioning, increased production catecholamine, decreased cholinergic glucocorticoid activity, cortisol. These factors lead chronic inflammation hinder normal leading depression, anxiety, cardiovascular disorders. brain, reactive oxygen species (ROS) by IL-6 stimulation cytochrome c oxidase inhibited nitric oxide, potent inhibitor. Proteins, lipids, phosphorylation enzymes, mtDNA are further disposed impairment mitochondria. Consequently, dysfunction exacerbates impairs DNA (mtDNA) or deletions mtDNA, increases intracellular Ca 2+ levels, changes fission/fusion morphology, lastly leads neuronal death. highlights multidisciplinary approaches intelligence. It will open new way technologies.

Язык: Английский

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FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas

Sensors, Год журнала: 2023, Номер 23(13), С. 6090 - 6090

Опубликована: Июль 2, 2023

A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate face mask protocol in public places. To achieve this goal, private dataset was created, including different images with and without masks. The trained to detect masks from real-time surveillance videos. detection (FMDNet) achieved promising of 99.0% terms accuracy violations (no mask) presented better capability compared other recent DL models such as FSA-Net, MobileNet V2, ResNet 24.03%, 5.0%, 24.10%, respectively. Meanwhile, lightweight had confidence score resource-constrained environment. can perform task environments at 41.72 frames per second (FPS). Thus, developed be applicable useful governments maintain rules SOP protocol.

Язык: Английский

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Efficacy of a vaginal suppository formulation prepared with Acacia arabica (Lam.) Willd. gum and Cinnamomum camphora (L.) J. Presl. in heavy menstrual bleeding analyzed using a machine learning technique

Frontiers in Pharmacology, Год журнала: 2024, Номер 15

Опубликована: Фев. 12, 2024

Objective: This study aims to determine the efficacy of Acacia arabica (Lam.) Willd. and Cinnamomum camphora (L.) J. Presl. vaginal suppository in addressing heavy menstrual bleeding (HMB) their impact on participants' health-related quality life (HRQoL) analyzed using machine learning algorithms. Method: A total 62 participants were enrolled a double-dummy, single-center study. They randomly assigned either group (SG), receiving formulation prepared with gum ( Gond Babul ) camphor from Kafoor through two suppositories (each weighing 3,500 mg) for 7 days at bedtime along oral placebo capsules, or tranexamic (TG), acid (500 twice day 5 during menstruation three consecutive cycles. The primary outcome was pictorial blood loss assessment chart (PBLAC) HMB, secondary outcomes included hemoglobin level SF-36 HRQoL questionnaire scores. Additionally, algorithms such as k-nearest neighbor (KNN), AdaBoost (AB), naive Bayes (NB), random forest (RF) classifiers employed analysis. Results: In SG TG, mean PBLAC score decreased 635.322 ± 504.23 67.70 22.37 512.93 283.57 97.96 39.25, respectively, post-intervention (TF3), demonstrating statistically significant difference p < 0.001). higher percentage achieved normal compared TG (93.5% vs 74.2%). showed considerable improvement scores (73.56%) (65.65%), no serious adverse events reported group. Notably, algorithms, particularly AB KNN, demonstrated highest accuracy within cross-validation models both outcomes. Conclusion: A. C. is effective, cost-effective, safe controlling HMB. botanical provides novel innovative alternative traditional interventions, promise an effective management approach

Язык: Английский

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Efficacy and classification of Sesamum indicum linn seeds with Rosa damascena mill oil in uncomplicated pelvic inflammatory disease using machine learning

Frontiers in Chemistry, Год журнала: 2024, Номер 12

Опубликована: Апрель 2, 2024

Background and objectives: As microbes are developing resistance to antibiotics, natural, botanical drugs or traditional herbal medicine presently being studied with an eye of great curiosity hope. Hence, complementary alternative treatments for uncomplicated pelvic inflammatory disease (uPID) explored their efficacy. Therefore, this study determined the therapeutic efficacy safety Sesamum indicum Linn seeds Rosa damascena Mill Oil in uPID standard control. Additionally, we analyzed data machine learning. Materials methods: We included 60 participants a double-blind, double-dummy, randomized standard-controlled study. Participants Sesame Rose oil group (SR group) ( n = 30) received 14 days course black sesame powder (5 gm) mixed rose (10 mL) per vaginum at bedtime once daily plus placebo capsules orally. The (SC), doxycycline 100 mg twice metronidazole 400 thrice orally same duration. primary outcome was clinical cure post-intervention visual analogue scale (VAS) lower abdominal pain (LAP), McCormack (McPS) abdominal-pelvic tenderness. secondary white blood cells (WBC) vaginal wet mount test, profile, health-related quality life assessed by SF-12. In addition, used AdaBoost (AB), Naïve Bayes (NB), Decision Tree (DT) classifiers analyze experimental data. Results: LAP McPS SR vs SC 82.85% 81.48% 83.85% 81.60% on Day 15 respectively. On 15, pus less than 10 were 86.6% 76.6% No adverse effects reported both groups. improvement total SF-12 score 30 82.79% 80.04% our Naive classifier based leave-one-out model achieved maximum accuracy (68.30%) classification groups uPID. Conclusion: concluded that is cost-effective, safer, efficacious curing Proposed treatment (test drug) could be substitute drug Female genital tract infections.

Язык: Английский

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Medical intelligence for anxiety research: Insights from genetics, hormones, implant science, and smart devices with future strategies

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Год журнала: 2024, Номер 14(6)

Опубликована: Авг. 4, 2024

Abstract This comprehensive review article embarks on an extensive exploration of anxiety research, navigating a multifaceted landscape that incorporates various disciplines, such as molecular genetics, hormonal influences, implant science, regenerative engineering, and real‐time cardiac signal analysis, all while harnessing the transformative potential medical intelligence [medical + artificial (AI)]. By addressing fundamental research questions, this study investigated foundations underlying disorders, shedding light intricate interplay genetic factors contributing to etiology progression anxiety. Furthermore, delves into emerging implications biomaterials, defibrillators, state‐of‐the‐art devices for elucidating their roles in diagnosis, treatment, patient management. A pivotal contribution is development AI‐driven model analysis. innovative approach offers promising avenue enhancing precision timeliness diagnosis monitoring. Leveraging machine learning AI techniques enables accurate classification persons with based data, thereby ushering new era personalized data‐driven mental health care. Identifying themes knowledge gaps lays foundation future directions roadmap scholars practitioners navigate field. In conclusion, serves vital resource, consolidating diverse perspectives fostering deeper understanding disorders from biological, technological standpoints, ultimately advancing clinical practice. categorized under: Application Areas > Health Care Science Technology Technologies Classification

Язык: Английский

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Intelligent Internet of Medical Things for Depression: Current Advancements, Challenges, and Trends

International Journal of Intelligent Systems, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

We investigated the fusion of Intelligent Internet Medical Things (IIoMT) with depression management, aiming to autonomously identify, monitor, and offer accurate advice without direct professional intervention. Addressing pivotal questions regarding IIoMT’s role in identification, its correlation stress anxiety, impact machine learning (ML) deep (DL) on depressive disorders, challenges potential prospects integrating management IIoMT, this research offers significant contributions. It integrates artificial intelligence (AI) (IoT) paradigms expand studies, highlighting data science modeling’s practical application for intelligent service delivery real‐world settings, emphasizing benefits within IoT. Furthermore, it outlines an IIoMT architecture gathering, analyzing, preempting employing advanced analytics enhance intelligence. The study also identifies current challenges, future trajectories, solutions domain, contributing scientific understanding management. evaluates 168 closely related articles from various databases, including Web Science (WoS) Google Scholar, after rejection repeated books. shows that there is 48% growth articles, mainly focusing symptoms, detection, classification. Similarly, most being conducted United States America, trend increasing other countries around globe. These results suggest essence automated monitoring, suggestions handling depression.

Язык: Английский

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Metaverse

Advances in medical technologies and clinical practice book series, Год журнала: 2023, Номер unknown, С. 93 - 158

Опубликована: Ноя. 10, 2023

The rise of the metaverse as a digital domain for diverse activities has birthed an innovative application known ‘metaverse virtual meditation.' This concept seamlessly merges technology and mindfulness, employing reality (VR) augmented (AR) to craft serene landscapes. These immersive settings, ranging from natural vistas abstract spaces, enable users overcome physical constraints distractions, facilitating stress reduction, emotional resilience. chapter navigates fusion contemplative practices, traditional meditation modern VR AR experiences. Stress heightened focus, inclusivity are among advantages highlighted. convergence visuals, biofeedback, brain-computer interfaces (BCIs), AI-driven personalization is explored tailored meditation. Design principles, interactive elements, components play crucial role in shaping tranquil environments.

Язык: Английский

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Unleashing the Power of AI in Communication Technology: Advances, Challenges, and Collaborative Prospects

Advanced technologies and societal change, Год журнала: 2024, Номер unknown, С. 211 - 226

Опубликована: Авг. 30, 2024

Язык: Английский

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Progress and research trends in lumpy skin disease based on the scientometric assessment – a review

Annals of Animal Science, Год журнала: 2024, Номер unknown

Опубликована: Сен. 7, 2024

Abstract Background Lumpy skin disease (LSD) has been a significant concern in veterinary medicine since its discovery. Despite decades of research, understanding the full spectrum this remains challenge. To address gap, comprehensive analysis existing body knowledge on LSD is essential. Bibliometric offers systematic approach towards mapping research landscape, identifying key contributors, and uncovering emerging trends research. Objective This study aims to conduct thorough bibliometric spanning from 1947 till present date order map domain LSD. The objective gain insights into global trends, identify influential explore collaboration networks, predict future outlook Method Data extracted Scopus database was used perform analysis. 341 relevant documents were selected for indicators, including publication numbers, citation counts, h-index, utilized assess contributions nations, organizations, authors, source titles. Additionally, cooperation networks between countries, authors visualized using VOSviewer tool. Results revealed increase output LSD, with notable growth rate 19.26%. Since discovery Zambia 1929, grown steadily, an average annual 5.21%. University Pretoria Federal Centre Animal Health emerged as most active institutions organizations Journal Virology identified cited journal, reflecting impact field, strong international observed United Kingdom South Africa. Conclusion provides valuable landscape highlighting networks. By reviewing enhances our serves foundation endeavours. findings will aid researchers navigating vast literature ultimately contributing advancements management strategies.

Язык: Английский

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A machine learning-based analysis for the effectiveness of online teaching and learning in Pakistan during COVID-19 lockdown

Work, Год журнала: 2025, Номер unknown

Опубликована: Янв. 15, 2025

Background The COVID-19 pandemic has significantly disrupted daily life and education, prompting institutions to adopt online teaching. Objective This study delves into the effectiveness of these methods during lockdown in Pakistan, employing machine learning techniques for data analysis. Methods A cross-sectional survey was conducted with 300 respondents using a semi-structured questionnaire assess perceptions education. Artificial intelligence analyzed specificity, sensitivity, accuracy, precision collected data. Results Among participants, 42.3% expressed satisfaction learning, while 49.3% preferred Zoom. Convenience noted 72% favoring classes between 8 AM 12 PM. revealed 87.33% felt placement activities were negatively impacted, 85% reported effects on individual growth. Additionally, 90.33% stated that their routines, 84.66% citing adverse physical health. Decision Tree classifier achieved highest accuracy at 86%. Overall, preferences leaned toward traditional in-person teaching despite methods. Conclusions highlights significant challenges transitioning emphasizing disruptions routines overall well-being. Notably, age gender did not influence growth or Finally, collaborative efforts among educators, policymakers, stakeholders are crucial ensuring equitable access quality education future crises.

Язык: Английский

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