Search Results (314)

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

Obstructive sleep apnea is a highly prevalent respiratory disease linked to increased morbidity and mortality, a reduced quality of life, and increased economic costs if not treated. Oral appliances are an emerging treatment option for obstructive sleep apnea. This review concluded that many oral appliances marketed as “custom” include modifications and prefabricated items, and therefore do not meet the definition of “custom” oral appliances. This misclassification could hinder the accurate characterization, evaluation, and appropriate prescription of oral appliances. To better inform the clinical utilization of custom oral appliances and to more closely align sleep medicine with the benefits of personalized medicine, we propose that the custom oral appliance classification be further refined into semi-custom and precision-custom categories. Full article

In the rapidly evolving field of biomedical engineering, hydrogels have emerged as highly versatile biomaterials that bridge biology and technology through their high water content, exceptional biocompatibility, and tunable mechanical properties. This review provides an integrated overview of both natural and synthetic hydrogels, examining their structural properties, fabrication methods, and broad biomedical applications, including drug delivery systems, tissue engineering, wound healing, and regenerative medicine. Natural hydrogels derived from sources such as alginate, gelatin, and chitosan are highlighted for their biodegradability and biocompatibility, though often limited by poor mechanical strength and batch variability. Conversely, synthetic hydrogels offer precise control over physical and chemical characteristics via advanced polymer chemistry, enabling customization for specific biomedical functions, yet may present challenges related to bioactivity and degradability. The review also explores intelligent hydrogel systems with stimuli-responsive and bioactive functionalities, emphasizing their role in next-generation healthcare solutions. In modern medicine, temperature-, pH-, enzyme-, light-, electric field-, magnetic field-, and glucose-responsive hydrogels are among the most promising “smart materials”. Their ability to respond to biological signals makes them uniquely suited for next-generation therapeutics, from responsive drug systems to adaptive tissue scaffolds. Key challenges such as scalability, clinical translation, and regulatory approval are discussed, underscoring the need for interdisciplinary collaboration and continued innovation. Overall, this review fosters a comprehensive understanding of hydrogel technologies and their transformative potential in enhancing patient care through advanced, adaptable, and responsive biomaterial systems. Full article

Despite recent advances in the treatment of obesity, lifestyle medicine remains foundational to the treatment of individuals with obesity, regardless of the modality chosen by the patient with the guidance of the clinician they are working with, including in conjunction with, as appropriate, anti-obesity medications and metabolic surgery. Lifestyle medicine involves the use of diet, exercise, sleep, stress, and other lifestyle modalities in the treatment of disease. Clinicians and health systems should, after a patient-centered discussion with the patient, do their best to ensure access to lifestyle treatments. Gold standard guidelines recommend intensive, multicomponent lifestyle change programs for obesity treatments with evidence-based diet and exercise counseling and established, theoretically driven behavior change components. Clinicians treating obesity should be aware of their own biases, make efforts to reduce stigmatizing experiences in their practice, and address weight stigma in their treatment plans as needed. A variety of dietary patterns can be used to support patients with obesity, and clinicians should make evidence-based but patient-centered recommendations that aim to maximize adherence. Diet and exercise can play an important role in reducing the side effects of treatment and optimizing outcomes in weight loss, attenuating the effects of metabolic adaptation, and weight maintenance. Exercise should be increased gradually to reduce injury with a goal of 200–300 min (approximately 3.3–5 h) of moderate to vigorous intensity exercise per week to maximize weight maintenance effects with exercise prescriptions customized to patients risks. A variety of practice models can be leveraged along with the use of an interdisciplinary team to provide lifestyle medicine care for those with obesity. Full article

Immunological factors have gained growing recognition as key contributors to recurrent pregnancy loss (RPL) after in vitro fertilization (IVF), representing a major challenge in reproductive medicine. RPL affects approximately 1–2% of women trying to conceive naturally and up to 10–15% of those undergoing IVF, where overall success rates remain around 30–40% per cycle. An imbalance in maternal immunological tolerance toward the semi-allogeneic fetus during pregnancy may lead to miscarriage and implantation failure. IVF-related ovarian stimulation and embryo modification offer additional immunological complications that can exacerbate existing immune dysregulation. Recent advances in reproductive immunology have significantly deepened our understanding of the immune mechanisms underlying RPL following IVF, particularly highlighting the roles of regulatory T cells (T regs), natural killer cells, cytokine dysregulation, and disruptions in maternal–fetal immune tolerance. In order to better customize therapies, this evaluation incorporates recently discovered immunological biomarkers and groups patients according to unique immune profiles. Beyond conventional treatments like intralipid therapy and intravenous immunoglobulin, it also examines new immunomodulatory medications that target certain immune pathways, such as precision immunotherapies and novel cytokine modulators. We also discuss the debates over immunological diagnostics and therapies, such as intralipid therapy, intravenous immunoglobulin, corticosteroids, and anticoagulants. The heterogeneity of patient immune profiles combined with a lack of strong evidence highlights the imperative for precision medicine to improve therapeutic consistency. Novel indicators for tailored immunotherapy and emerging treatments that target particular immune pathways have encouraging opportunities to increase pregnancy success rates. Improving management approaches requires that future research prioritize large-scale clinical trials and the development of standardized immunological assessments. This review addresses the immunological factors in RPL during IVF, emphasizing underlying mechanisms, ongoing controversies, and novel therapeutic approaches to inform researchers and clinicians. Full article

Cerebral edema is the abnormal accumulation of fluid in any of the tissue compartments of the cerebral parenchyma. It remains a significant challenge in neurotrauma care because it contributes to secondary brain injury, affecting prognosis. This review analyzes the recent literature, including foundational studies, to describe the mechanisms of distinct types of cerebral edema following traumatic brain injury (TBI). Emerging concepts, such as the role of the glymphatic system and heme-derived inflammasomes, offer new insights into new types of edemas, differentiated by pathogenesis and potential treatments. Recent advancements in understanding these molecular mechanisms can improve therapeutic strategies, facilitating a better approach in the era of precision and personalized medicine. Although there has been notable progress, a proposal to customize treatments for diverse types of edemas is necessary to improve outcomes following traumatic brain injury. In this review, we describe the current subtypes of post-traumatic brain edemas and link them to a specific management approach. Full article

Background: Automated semi-solid extrusion (SSE) material deposition is a promising new technology for preparing personalized medicines for different patient groups and veterinary applications. The technology enables the preparation of custom-made oral elastic gel tablets of active pharmaceutical ingredient (API) by using a semi-solid polymeric printing ink. Methods: An automated SSE material deposition method was used for generating chewable gel tablets loaded with propranolol hydrochloride (-HCl) at three different API content levels (3.0 mg, 4.0 mg, 5.0 mg). The physical appearance, surface morphology, dimensions, mass and mass variation, process-derived solid-state changes, mechanical properties, and in-vitro drug release of the gel tablets were studied. Results: The inclusion of API (1% w/w) in the semi-solid CuraBlend^TM printing mixture decreased viscosity and increased fluidity, thus promoting the spreading of the mixture on the printed (material deposition) bed and the printing performance of the gel tablets. The printed gel tablets were elastic, soft, jelly-like, chewable preparations. The mechanical properties of the gel tablets were dependent on the printing ink composition (i.e., with or without propranolol HCl). The maximum load for the final deformation of the CuraBlend™-API (3.0 mg) gel tablets was very uniform, ranging from 73 N to 80 N. The in-vitro dissolution test showed that more than 85% of the drug load was released within 15–20 min, thus verifying the immediate-release behavior of these drug preparations. Conclusions: Automated SSE material deposition as a modified 3D printing method is a feasible technology for preparing customized oral chewable gel tablets of propranolol HCl. Full article

Background/Objectives: Limb malalignment disrupts physiological joint forces and predisposes individuals to the development of osteoarthritis. Surgical interventions such as distal femur or high tibial osteotomy aim to restore mechanical balance on weight-bearing joints, thereby reducing long-term morbidity. Accurate alignment is crucial since it cannot be adjusted after stabilization with plates and screws. Recent advances in personalized medicine offer the opportunity to tailor surgical corrections to each patient’s unique anatomy and biomechanical profile. This study evaluates the benefits of 3D planning and patient-specific cutting guides over traditional 2D planning with standard implants for alignment correction procedures. Methods: We assessed limb alignment parameters pre- and postoperatively in patients with varus and valgus lower limb malalignment undergoing acute realignment surgery. The cohort included 23 opening-wedge high tibial osteotomies and 28 opening-wedge distal femur osteotomies. We compared the accuracy of postoperative alignment parameters between patients undergoing traditional 2D preoperative X-ray planning and those using 3D reconstructions of CT data. Outcome measures included mechanical axis deviation and tibiofemoral angles. Results: 3D reconstructions of computerized tomography data and patient-specific cutting guides significantly reduced the variation in postoperative limb alignment parameters relative to preoperative goals. In contrast, traditional 2D planning with standard non-custom implants resulted in higher deviations from the targeted alignment. Conclusions: Utilizing 3D CT reconstructions and patient-specific cutting guides enhances the accuracy of postoperative limb realignment compared to traditional 2D X-ray planning with standard non-custom implants. Patient-specific instrumentation and personalized approaches represent a key step toward precision orthopedic surgery, tailoring correction strategies to individual patient anatomy and potentially improving long-term joint health. This improvement may reduce the morbidity associated with lower limb malalignment and delay the onset of osteoarthritis. Level of Evidence: Therapeutic Level III. Full article

Modern oncology increasingly relies on personalized strategies that aim to customize medical interventions, using both tumor biology and clinical features to enhance efficacy and minimize adverse effects. In recent years, precision medicine has been implemented as part of systemic therapies; however, its integration into radiation therapy (RT) is still a work in progress. Conventional RT treatment plans are based on the Linear Quadratic (LQ) model and utilize standardized alpha and beta ratios (α/β), which ignore the high variability in terms of treatment response between and within patients. Recent advances in radiobiology, as well as general medical technologies, have also driven a shift toward more tailored approaches, including in RT. This review provides an overview of current knowledge and future perspectives for the personalization of RT, highlighting the role of tumor and patient-specific biomarkers, advanced imaging techniques, and novel therapeutic approaches. As an alternative to conventional RT modalities, hadron therapy and Flash RT are discussed as innovative approaches with the potential to improve tumor targeting while sparing normal tissues. Furthermore, the synergistic combination of RT with immunotherapy is discussed as a potential strategy to support antitumor immune responses and overcome resistance. By integrating biological insights, technological innovation, and clinical expertise, personalized radiation therapy may significantly advance the precision oncology paradigm. Full article

Objective: Esophageal cancer (EC) is difficult to visually identify, rendering early detection crucial to avert the advancement and decline of the patient’s health. Methodology: This work aimed to acquire spectral information from EC images via Spectrum-Aided Visual Enhancer (SAVE) technology, which improves imaging beyond the limitations of conventional White-Light Imaging (WLI). The hyperspectral data acquired using SAVE were examined utilizing sophisticated deep learning methodologies, incorporating models such as YOLOv8, YOLOv7, YOLOv6, YOLOv5, Scaled YOLOv4, and YOLOv3. The models were assessed to create a reliable detection framework for accurately identifying the stage and location of malignant lesions. Results: The comparative examination of these models demonstrated that the SAVE method regularly surpassed WLI for specificity, sensitivity, and overall diagnostic efficacy. Significantly, SAVE improved precision and F1 scores for the majority of the models, which are essential measures for enhancing patient care and customizing effective medicines. Among the evaluated models, YOLOv8 showed exceptional performance. YOLOv8 demonstrated increased sensitivity to squamous cell carcinomas (SCCs), but YOLOv5 provided reliable outcomes across many situations, underscoring its adaptability. Conclusions: These findings highlight the clinical importance of combining SAVE technology with deep learning models for esophageal cancer screening. The enhanced diagnostic accuracy provided by SAVE, especially when integrated with CAD models, offers potential for improving early detection, precise diagnosis, and tailored treatment approaches in clinically pertinent scenarios. Full article

Somatotyping is essential for assessing body composition in sports science, anthropology, and medicine. Traditional methods, such as the Heath–Carter approach, rely on manual measurements, which can be prone to errors and variability. This study evaluates the validity and reliability of 3D body scanning as an alternative to manual somatotyping. A total of 117 participants (49 males, 68 females) aged 18 to 27 years were assessed using both traditional anthropometric methods and a full-body 3D scanning system (TC² NX-16). The three somatotype components (ectomorphy, mesomorphy, and endomorphy) were calculated using the Heath–Carter method. A custom-developed application processed the scanned data to compute somatotype values. The results were compared using statistical metrics, including intraclass correlation coefficients (ICCs) and Bland–Altman analysis. The 3D scanning method showed high agreement (87.18%) with manual measurements. Minor discrepancies were observed particularly in the endomorphic component, which was slightly overestimated by 3D scanning. Mesomorphic and ectomorphic components exhibited minimal differences. Statistical analyses confirmed strong reliability with ICC values exceeding 0.87. Conclusions: Full-body 3D scanning is a viable, non-invasive, and efficient alternative to traditional somatotyping methods. Despite minor differences in endomorphy estimation, the overall accuracy and reliability supports its use in sports science, health monitoring, and anthropometric research. Future studies should refine predictive models for endomorphy estimation and integrate AI-driven classification techniques to enhance precision. Full article

The recent advancement of 3D-printed drugs is an emerging technology that has the potential for effective and safe oral delivery of personalized treatment regimens to patients, replacing the current “one size fits all” philosophy. The objective of this literature review is to highlight the importance of 3D-printing technology in the development of personalized treatments, focusing on Levetiracetam, the first FDA-approved 3D-printed drug, for the treatment of epilepsy. Levetiracetam serves as an ideal paradigm for exploring how precision medicine and 3D printing can be applied to improve treatment outcomes for other complex diseases such as diabetes, cardiovascular diseases, and cancer. 3D printing enables precise dosage and time-release profiles by modifying factors such as shape and size, and the combination of active pharmaceutical ingredients (APIs) and excipients, ensuring consistent therapeutic levels over the treatment period. Design of oral tablets with multiple compartments allows for simultaneous treatment with multiple APIs, each one with a different release profile, minimizing drug–drug interactions and side effects. This technology also supports on-demand production, making it particularly beneficial in resource-limited or urgent situations, and offers the flexibility to customize dosage forms. Additive manufacturing could be an important tool for developing personalized treatments to address the diverse medical needs of patients with complex diseases. Therefore, there is a need for more 3D-printed FDA-approved drugs in the biopharmaceutical industry to enable personalized treatment, improved patient compliance, and precise drug release control. Full article

Background/Objectives: The percutaneous insertion of an external ventricular drain (EVD) is a common neurosurgical procedure that is crucial in managing acute brain injuries because of the drain’s role in monitoring intracranial pressure and draining cerebrospinal fluid. The primary indication is acute hydrocephalus, which often results from subarachnoid hemorrhage, intracranial hemorrhage, traumatic brain injury, stroke, or infection. Standard EVD placement targets the frontal horn of the lateral ventricle. However, complications such as hemorrhage, infection, and catheter occlusion frequently arise, with occlusion rates ranging from 19% to 47%. Occlusion can lead to increased intracranial pressure, necessitating interventions such as saline flushes or fibrinolytic drug administration. The placement of an EVD is a very specific choice that must be tailored to the individual patient, often in scenarios in which multiple interpretations of the data are possible: the question of which patient is eligible for EVD placement may be subjective. Intraventricular fibrinolysis (IVF) with urokinase-type plasminogen activator (uPA) or tissue-type plasminogen activator is used with the aim of lysing intraventricular clots and preventing EVD occlusion. Despite numerous studies, conclusive evidence on their efficacy is lacking. The CLEAR III trial confirmed the safety of IVF but showed uncertain benefits in neurological outcomes. Given the limited literature on uPA, this study evaluates its intrathecal administration for the prevention of EVD occlusion. Not all therapies are appropriate for all patients, and customizing strategies is often the right way to get the best result. Methods: This retrospective study analyzed 20 patients with EVDs receiving intrathecal uPA. The patients had a mean age of 56.4 years, with 95% presenting with hydrocephalus and 80% presenting with intraventricular hemorrhage. uPA dosages varied (25,000–100,000 IU), with an average of 3.9 doses per patient. Results: IVF effectively maintained EVD patency in 95% of cases. One patient experienced asymptomatic bleeding, while four (20%) developed post-treatment infections, the development of which was potentially influenced by the prolonged duration of EVD retention (>21 days). Analysis of Graeb scores showed faster clot resolution with early uPA administration. A higher initial Graeb score correlated with increased total uPA load but not with mortality or discharge outcomes. Although infection rates were slightly higher than in CLEAR III, multiple confounding factors, including duration of EVD retention and bilateral placement, were present. Conclusions: This study supports the feasibility and safety of intrathecal uPA administration for management of EVD occlusion in certain contexts. The appropriate choice in the context of ‘personalized medicine’ must necessarily consider the risk–benefit ratio. Full article

Advances in pharmaceutical technology have positioned 3D printing and bioprinting as promising tools for developing personalized drug therapies. These innovations may redefine compounding practices by enabling precise, patient-specific drug formulations. Evaluating pharmacists’ readiness to adopt such technologies is therefore becoming increasingly important. Aim: The aim of this study is to investigate pharmacists’ knowledge, attitudes, and perceived barriers regarding the application of 3D printing and bioprinting technologies, as well as their perspectives on the regulation and implementation of these technologies in the context of personalized pharmacy. Materials and Methods: A custom-designed questionnaire was developed for the purposes of this pilot study, based on a review of the existing literature and informed by expert consultation to ensure conceptual relevance and clarity. The survey was conducted between September and December 2024. The data collection instrument comprises three main sections: (1) sociodemographic and professional characteristics, (2) knowledge regarding the applications of 3D printing and bioprinting in pharmacy, and (3) attitudes toward the regulatory framework and implementation of these technologies. Results: A total of 353 respondents participated, and 65.5% of them (n = 231) correctly distinguished between the concepts of “3D printing” and “bioprinting.” More than 25% (n = 88) were uncertain, and 8.5% (n = 30) were unable to differentiate between the two. Regarding the perceived benefits of personalized pharmacy, 83% (n = 293) of participants identified “the creation of personalized medications tailored to individual needs” as the main advantage, while 66% (n = 233) highlighted the “optimization of drug concentration to enhance therapeutic efficacy and minimize toxicity and adverse effects.” Approximately 60% (n = 210) of the pharmacists surveyed believed that the introduction of 3D-bioprinted pharmaceuticals would have a positive impact on the on-site preparation of customized drug formulations in community and hospital pharmacies. Lack of regulatory guidance and unresolved ethical concerns were identified as primary barriers. Notably, over 40% (n = 142) of respondents expressed concern that patients could be subjected to treatment approaches resembling “laboratory experimentation.” Nearly 90% (n = 317) of participants recognized the need for specialized training and expressed a willingness to engage in such educational initiatives. Conclusions: Three-dimensional printing and bioprinting technologies are considered cutting-edge instruments that may contribute to the advancement of pharmaceutical practice and industry, particularly in the field of personalized medicine. However, respondents’ views suggest that successful integration may require improved pharmacist awareness and targeted educational initiatives, along with the development and adaptation of appropriate regulatory frameworks to accommodate these novel technologies in drug design and compounding. Full article

Histopathological images represent a valuable data source for pathologists, who can provide clinicians with essential landmarks for complex pathologies. The development of sophisticated computational models for histopathological images has received significant attention in recent years, but most of them rely on free datasets. Materials and Methods: Motivated by this drawback, the authors created an original histopathological image dataset that resulted from an animal experimental model, acquiring images from normal female rats/rats with experimentally induced diabetes mellitus (DM)/rats who received an antidiabetic therapy with a synthetic compound (AD_SC). Images were acquired from vaginal, uterine, and ovarian samples from both MD and AD_DC specimens. The experiment received the approval of the Medical Ethics Committee of the “Gr. T. Popa” University of Medicine and Pharmacy, Iași, Romania (Approval No. 169/22.03.2022). The novelty of the study consists of the following aspects. The first is the use of a diabetes-induced animal model to evaluate the impact of an antidiabetic therapy with a synthetic compound in female rats, focusing on three distinct organs of the reproductive system (vagina, ovary, and uterus), to provide a more comprehensive understanding of how diabetes affects female reproductive health as a whole. The second comprises image classification with a custom-built convolutional neural network (CB-CNN), the extraction of textural features (contrast, entropy, energy, and homogeneity), and their classification with PyCaret Auto Machine Learning (AutoML). Results: Experimental findings indicate that uterine tissue, both for MD and AD_DC, can be diagnosed with an accuracy of 94.5% and 85.8%, respectively. The Linear Discriminant Analysis (LDA) classifier features indicate a high accuracy of 86.3% when supplied with features extracted from vaginal tissue. Conclusions: Our research underscores the efficacy of classifying with two AI algorithms, CNN and machine learning. Full article