Early Effective Treatment Research Articles

Identification of an ADME-related gene for forecasting the prognosis and responding to immunotherapy in sarcomas

There are more than 170 subtypes of sarcomas (SARC), which pose a challenge for diagnosis and patient management. Relatively simple or complex karyotypes play an indispensable role in the early diagnosis and effective treatment of SARC. The genes related to absorption, distribution, metabolism, and excretion (ADME) of a drug can serve as prognostic biomarkers of cancer and potential drug targets. In this study, a risk score signature was created. The SARC cohort was downloaded from The Cancer Genome Atlas (TCGA) database, and divided into high-risk group and low-risk group according to the median value of risk score. Compared with high-risk group, low-risk group has a longer survival time, which is also verified in osteosarcoma cohort from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. In addition, the relationship between the signature and immunophenotypes, including status of immune cell infiltration and immune checkpoint expression, was explored. Then, we found that high-risk group is in immunosuppressive status. Finally, we verified that PPARD played a role as a carcinogen in osteosarcoma, which provided a direction for targeted treatment of osteosarcoma in the future. Generally speaking, the signature can not only help clinicians predict the prognosis of patients with SARC, but also provide a theoretical basis for developing more effective targeted drugs in the future.

Respiratory complications of neurological diseases

Objectives: This study aims to raise awareness toward early diagnosis, effective treatment, and prevention of respiratory complications in patients with neurological diseases. The goal is to improve patients’ overall condition and reduce mortality rates. Materials and Methods: This study is a prospective observational study conducted at Damascus Hospital, Damascus, Syria, from October 2022 to March 2023. It includes (100) patients diagnosed with neurological diseases who developed respiratory complications. Their ages ranged from 18 to 89 years, with an equal gender distribution. Data analysis was performed using SPSS statistics version 19. Results: This study revealed that cerebrovascular accidents cause aspiration pneumonia. The most frequent chest X-ray and thoracic computed tomography findings showed pulmonary densities and infiltrates. Subsequently, arterial blood gas results showed type 2 respiratory failure. Many patients experienced regression of respiratory complications, and the mortality rate was low, indicating the efficacy of treatments. Conclusion: Respiratory complications significantly contribute to mortality in patients with neurological diseases. The study emphasizes the importance of early diagnosis and preventive measures such as elevating the patient’s bed and periodic suctioning of secretions. Placing patients in specialized neurological intensive care units for tailored treatment is advised.

Risk factor analysis and risk prediction study of obesity in steelworkers: model development based on an occupational health examination cohort dataset

BackgroundObesity is increasingly recognized as a grave public health concern globally. It is associated with prevalent diseases including coronary heart disease, fatty liver, type 2 diabetes, and dyslipidemia. Prior research has identified demographic, socioeconomic, lifestyle, and genetic factors as contributors to obesity. Nevertheless, the influence of occupational risk factors on obesity among workers remains under-explored. Investigating risk factors specific to steelworkers is crucial for early detection, prediction, and effective intervention, thereby safeguarding their health.MethodsThis research utilized a cohort study examining health impacts on workers in an iron and steel company in Hebei Province, China. The study involved 5469 participants. By univariate analysis, multifactor analysis, and review of relevant literature, predictor variables were found. Three predictive models—XG Boost, Support Vector Machine (SVM), and Random Forest (RF)—were employed.ResultsUnivariate analysis and cox proportional hazard regression modeling identified age, gender, smoking and drinking habits, dietary score, physical activity, shift work, exposure to high temperatures, occupational stress, and carbon monoxide exposure as key factors in the development of obesity in steelworkers. Test results indicated accuracies of 0.819, 0.868, and 0.872 for XG Boost, SVM, and RF respectively. Precision rates were 0.571, 0.696, and 0.765, while recall rates were 0.333, 0.592, and 0.481. The models achieved AUCs of 0.849, 0.908, and 0.912, with Brier scores of 0.128, 0.105, and 0.104, log losses of 0.409, 0.349, and 0.345, and calibration-in-the-large of 0.058, 0.054, and 0.051, respectively. Among these, the Random Forest model demonstrated superior performance.ConclusionsThe research indicates that obesity in steelworkers results from a combination of occupational and lifestyle factors. Of the models tested, the Random Forest model exhibited superior predictive ability, highlighting its significant practical application.

BCG induced lower urinary tract symptoms during treatment for NMIBC-Mechanisms and management strategies.

Non-muscle invasive bladder cancer (NMIBC) accounts for ~70-75% of total bladder cancer tumors and requires effective early intervention to avert progression. The cornerstone of high-risk NMIBC treatment involves trans-urethral resection of the tumor followed by intravesical Bacillus Calmette-Guerin (BCG) immunotherapy. However, BCG therapy is commonly accompanied by significant lower urinary tract symptoms (LUTS) including urinary urgency, urinary frequency, dysuria, and pelvic pain which can undermine treatment adherence and clinical outcomes. Despite this burden, the mechanisms underlying the development of BCG-induced LUTS have yet to be characterized. This review provides a unique perspective on the mechanisms thought to be responsible for the development of BCG-induced LUTS by focussing on the sensory nerves responsible for bladder sensory transduction. This review focuses on how the physiological response to BCG, including inflammation, urothelial permeability, and direct interactions between BCG and sensory nerves could drive bladder afferent sensitization leading to the development of LUTS. Additionally, this review provides an up-to-date summary of the latest clinical data exploring interventions to relieve BCG-induced LUTS, including therapeutic targeting of bladder contractions, inflammation, increased bladder permeability, and direct inhibition of bladder sensory signaling. Addressing the clinical burden of BCG-induced LUTS holds significant potential to enhance patient quality of life, treatment compliance, and overall outcomes in NMIBC management. However, the lack of knowledge on the pathophysiological mechanisms that drive BCG-induced LUTS has limited the development of novel and efficacious therapeutic options. Further research is urgently required to unravel the mechanisms that drive BCG-induced LUTS.

Pleural Effusion Associated with Ulcerative Colitis: A Case Report

Background: Extraintestinal manifestations associated with IBD may involve the skin, joints, hepatobiliary tract, eye, kidney, and rarely pancreas and respiratory systems; as well as risks for venous thrombosis. They may be present at diagnosis or develop later in the disease course. Pulmonary complications of IBD include airway inflammation, parenchymal lung disease, serositis, thromboembolic disease, and drug-induced lung toxicity. Case Presentation: We report a case of a 41-year-old lady with ulcerative colitis who presented to the hospital with respiratory symptoms. Physical examination and imagery lead to the diagnosis of acute pleural effusion. Later on, the patient developed gastrointestinal symptoms. Colonoscopy and biopsy showed that she has Ulcerative Colitis. Which is a very rare presentation of EIM of IBD. The patient improved on steroids and mesalamine and pleural effusion resolved. Conclusion: In conclusion, pulmonary involvement should be considered when pleurisy develops in UC. Early diagnosis and effective treatment are important for these patients.

Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning.

Cervical cancer is a leading cause of women's mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification-handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women's health and healthcare systems.

Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma.

PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.

An in situ forming gelatin-based hydrogel loaded with soluble amniotic membrane promotes full-thickness wound regeneration in rats.

Early effective treatment and appropriate coverage are vital for full-thickness wounds. Amnion membrane-derived products have recently emerged in tissue engineering. However, the optimal concentration, carrier for controlled release, and handling have remained challenges. This study aims to develop and optimize an in situ forming, amniotic-based hydrogel for wound healing. Here, a composite matrix was fabricated with gelatin hydrogel modified with methacrylate functional group conjugated (GelMA) and keratose (wt.1%), loaded with mesenchymal stem cells (MSCs, 1×105 cell/ml) and optimized soluble amniotic membrane (SAM, 0.5 mg/ml). The physicochemical properties of the final subject were evaluated in vitro and in vivo environments. The results of the in vitro assay demonstrated that conjugation of the methacryloyl group with gelatin resulted in the formation of GelMA hydrogel (26.7±1.2 kPa) with higher mechanical stability. Modification of GelMA with a glycosaminoglycan sulfate (Keratose) increased controlled delivery of SAM (47.3% vs. 84.3%). Metabolic activity (93%) and proliferation (21.2 ± 1.5 µg/ml) of MSCs encapsulated in hydrogel improved by incorporation of SAM (0.5 mg/ml). Furthermore, the migration of fibroblasts was facilitated in the scratched assay by SAM (0.5 mg/ml)/MSCs (1×105 cell/ml) conditioned medium. The GelMA hydrogel groupes revealed regeneration of full-thickness skin defects in rats after 3 weeks due to the high angiogenesis (6.3 ± 0.3), cell migration, and epithelialization. The results indicated in situ forming and tunable GelMA hydrogels containing SAM and MSCs could be used as efficient substrates for full-thickness wound regeneration.

A brief review on the nanomaterials-based detection of CKD biomarkers

Chronic kidney disease (CKD) imposes a significant worldwide medical burden, exacerbated by the often limited efficacy of current treatments. The future prevention and management of CKD critically rely on early detection and effective intervention. Nanomaterials (NMs), such as fluorescence carbon dots (CDs), quantum dots (QDs), and metal-based NMs, emerge as unique and highly sensitive probe materials expected to play a substantial role in the precise identification of CKD biomarkers. NMs’ design makes it possible to manage properties including size, shape, charge, and targeting ligands, which enhances the biological compatibility and availability of medicines. Consequently, the rise of NMs in medicine has brought about fresh approaches to CKD diagnosis. This review explores the utility of the NMs for the rapid identification of CKD biomarkers.

Application of carbon-based nanomaterials in Alzheimer's disease.

Alzheimer's disease (AD) is a chronic, progressive neurodegenerative disorder marked by permanent impairment of brain function across the whole brain. This condition results in a progressive deterioration of cognitive function in patients and is frequently associated with psychological symptoms such as agitation and anxiety, imposing a significant burden on both patients and their families. Nanomaterials possess numerous distinctive physical and chemical features that render them extensively utilized. In the biomedical domain, nanomaterials can be utilized for disease prevention and therapy, including medication delivery systems, biosensors, and tissue engineering. This article explores the etiology and potential molecular processes of AD, as well as the application of carbon-based nanomaterials in the diagnosis and treatment of AD. Some of such nanomaterials are carbon quantum dots, carbon nanotubes, and graphene, among others. These materials possess distinctive physicochemical features that render them highly promising for applications in biosensing, drug delivery, neuroprotection, and photothermal treatment. In addition, this review explored various therapeutic approaches for AD in terms of reducing inflammation, preventing oxidative damage, and inhibiting Aβ aggregation. The advent of carbon nanomaterials in nanotechnology has facilitated the development of novel treatment approaches for Alzheimer's disease. These strategies provide promising approaches for early diagnosis, effective intervention and neuroprotection of the disease.

Age-Related Dysfunction in Balance: A Comprehensive Review of Causes, Consequences, and Interventions.

This review delves into the multifaceted aspects of age-related balance changes, highlighting their prevalence, underlying causes, and the impact they have on the elderly population. Central to this discussion is the exploration of various physiological changes that occur with aging, such as alterations in the vestibular, visual, proprioceptive systems, and musculoskeletal degeneration. We examine the role of neurological disorders, cognitive decline, and medication side effects in exacerbating balance issues. The review underscores the significance of early detection and effective intervention strategies in mitigating the risks associated with balance problems, such as falls and reduced mobility. It discusses the effectiveness of diverse intervention strategies, including exercise programs, rehabilitation techniques, and technological advancements like virtual reality, wearable devices, and telemedicine. Additionally, the review stresses the importance of a holistic approach in managing balance disorders, encompassing medication review, addressing comorbidities, and environmental modifications. The paper also presents future research directions, emphasizing the need for a deeper understanding of the complex mechanisms underlying balance changes with aging and the potential of emerging technologies and interdisciplinary approaches in enhancing assessment and intervention methods. This comprehensive review aims to provide valuable insights for healthcare providers, researchers, and policymakers in developing targeted strategies to improve the quality of life and ensure the well-being of the aging population.

Advanced Camera-Based Scoliosis Screening via Deep Learning Detection and Fusion of Trunk, Limb, and Skeleton Features.

Scoliosis significantly impacts quality of life, highlighting the need for effective early scoliosis screening (SS) and intervention. However, current SS methods often involve physical contact, undressing, or radiation exposure. This study introduces an innovative, non-invasive SS approach utilizing a monocular RGB camera that eliminates the need for undressing, sensor attachment, and radiation exposure. We introduce a novel approach that employs Parameterized Human 3D Reconstruction (PH3DR) to reconstruct 3D human models, thereby effectively eliminating clothing obstructions, seamlessly integrated with an ISANet segmentation network, which has been enhanced by Multi-Scale Fusion Attention (MSFA) module we proposed for facilitating the segmentation of distinct human trunk and limb features (HTLF), capturing body surface asymmetries related to scoliosis. Additionally, we propose a Swin Transformer-enhanced CMU-Pose to extract human skeleton features (HSF), identifying skeletal asymmetries crucial for SS. Finally, we develop a fusion model that integrates the HTLF and HSF, combining surface morphology and skeletal features to improve the precision of SS. The experiments demonstrated that PH3DR and MSFA significantly improved the segmentation and extraction of HTLF, whereas ST-based CMU-Pose substantially enhanced the extraction of HSF. Our final model achieved a comparable F1 (0.895 ±0.014) to the best-performing baseline model, with only 0.79% of the parameters and 1.64% of the FLOPs, achieving 36 FPS-significantly higher than the best-performing baseline model (10 FPS). Moreover, our model outperformed two spine surgeons, one less experienced and the other moderately experienced. With its patient-friendly, privacy-preserving, and easily deployable solution, this approach is particularly well-suited for early SS and routine monitoring.

Epidemiological Characteristics of Falls from the National Injury Surveillance System - China, 2019-2022.

This study describes and analyzes the distribution of fall cases documented in the National Injury Surveillance System (NISS) from 2019 to 2022. Fall data from the NISS (2019-2022) were descriptively analyzed by gender and age. The NISS in China reported 2,324,577 fall cases from 2019 to 2022. Falls mostly happened at home (39.41%). Leisure activities (39.40%) were the most common cause of falls. Among these cases, 45.22% caused bruises, 31.57% lower limb injuries, while a substantial 73.41% constituted mild injuries. Age-related epidemiological patterns of falls revealed distinct trends. Among 0-9-year-olds, falls predominantly occurred at home (56.71%), with high head injury rates, particularly in infants (72.64%). Adolescents (10-19 years old) were prone to falls in school and school-related areas (37.12%) and during sports activities (27.37%). Elderly individuals (>60 years old) experienced higher fracture rates (36.84%) and an age-dependent increase in head injuries and severe harm. Falls are the most prevalent injury category, exhibiting distinct characteristics across age groups. To address this, tailored public health strategies and interventions are essential, particularly for children, adolescents, and adults aged 60 years and older. Effective early prevention and intervention methods are paramount for these high-risk fall populations.

Circ_0008146 Exacerbates Ferroptosis via Regulating the miR-342-5p/ACSL4 Axis After Cerebral Ischemic/Reperfusion.

Acute ischemic stroke (AIS) has seriously threatened people's health worldwide and there is an urge need for early diagnosis and effective treatment of AIS. This research intended to clarify the regulatory role of circ_0008146/miR-342-5p/ACSL4 axis in AIS. High-throughput small RNA sequencing analysis was adapted to identify differentially expressed miRNAs between the AIS and control group. The circ_0008146, miR-342-5p, and ACSL4 levels were detected by qRT-PCR. Middle cerebral artery occlusion/reperfusion (MCAO/R) models were constructed in C57BL/6J mice. Assay kits were used to determine Fe2+ levels and a battery of oxidative stress and lipid peroxidation indicators, including ROS, MDA, LPO, SOD and GSH/GSSG ratio. The protein levels of ACSL4 were measured by Western blot. The behavioral function was assessed using neurobehavioral tests. TTC staining was employed to visualize infarction size. Nissl staining was adapted to detect histopathological changes. Receiver operating characteristic curve and correlation analysis were applied to investigate the clinical value and association of miR-342-5p and ACSL4. A total of 44 AIS patients and 49 healthy controls were enrolled in our study. The small RNA sequencing unveiled a significant decrease in miR-342-5p levels in AIS patients. MiR-342-5p inhibited oxidative stress and RSL3-induced ferroptosis after cerebral ischemic/reperfusion injury in vivo by targeting ferroptosis-related gene ACSL4. Circ_0008146 acted as a sponge of miR-342-5p, and overexpression of circ_0008146 increased neurological deficits and brain injury in mice. Circ_0008146 contributed to ferroptosis in cerebral infarction via sponging miR-342-5p to regulate ACSL4. Plasma miR-342-5p and ACSL4 demonstrated significant correlation and good diagnostic value for AIS patients. This study provides the first in vivo evidence to show that circ_0008146 exacerbates neuronal ferroptosis after AIS via the miR-342-5p/ACSL4 axis. Furthermore, miR-342-5p/ACSL4 axis holds promise as a viable therapeutic target and practical biomarkers for AIS patients.

Analysis and interpretability of machine learning models to classify thyroid disease.

Thyroid disease classification plays a crucial role in early diagnosis and effective treatment of thyroid disorders. Machine learning (ML) techniques have demonstrated remarkable potential in this domain, offering accurate and efficient diagnostic tools. Most of the real-life datasets have imbalanced characteristics that hamper the overall performance of the classifiers. Existing data balancing techniques process the whole dataset at a time that sometimes causes overfitting and underfitting. However, the complexity of some ML models, often referred to as "black boxes," raises concerns about their interpretability and clinical applicability. This paper presents a comprehensive study focused on the analysis and interpretability of various ML models for classifying thyroid diseases. In our work, we first applied a new data-balancing mechanism using a clustering technique and then analyzed the performance of different ML algorithms. To address the interpretability challenge, we explored techniques for model explanation and feature importance analysis using eXplainable Artificial Intelligence (XAI) tools globally as well as locally. Finally, the XAI results are validated with the domain experts. Experimental results have shown that our proposed mechanism is efficient in diagnosing thyroid disease and can explain the models effectively. The findings can contribute to bridging the gap between adopting advanced ML techniques and the clinical requirements of transparency and accountability in diagnostic decision-making.

Risk Factors Analysis and Prediction of Rotator Cuff Tears: A Retrospective Study.

Rotator cuff tears (RCTs) are a major cause of shoulder pain and disability, affecting millions worldwide. Understanding the risk factors and developing reliable predictive measures for RCTs is essential for early diagnosis, targeted prevention, and effective treatment of this patient population. This study seeks to enhance our understanding by analyzing the acromiohumeral distance (AHD) and Constant-Murley Score (CMS) in patients with and without RCTs, thereby aiding the development of a predictive model aimed at improving clinical outcomes and prevention strategies in rotator cuff pathology. This retrospective analysis involved 201 patients with shoulder pain, categorized into RCT (n = 72) and no RCTs (N-RCTs, n = 129) groups based on Magnetic Resonance Imaging (MRI) findings. We compared demographics, AHD, CMS, and rotator cuff status between groups and utilized logistic regression for identifying RCT predictors, leading to the development of a multifactorial predictive model. The mean AHD was 6.60 ± 1.12 mm. The RCT group showed a marginally higher AHD than the N-RCT group (p = 0.669). CMS scores were significantly lower in the RCT group (p < 0.001). Dominant side involvement (Odds Ratio (OR) 2.244), type III acromion (OR 6.106), and lower CMS (OR 0.938) significantly correlated with RCTs. The predictive model demonstrated an area under the curve (AUC) of 0.701 for RCT diagnosis. Reduced CMS, dominance of the affected side, and type III acromion emerged as key risk factors for RCTs. Our predictive model, incorporating these factors, holds promise for RCT diagnosis, with future studies needed for further validation.

Nanoenzymes: A Radiant Hope for the Early Diagnosis and Effective Treatment of Breast and Ovarian Cancers.

Breast and ovarian cancers, despite having chemotherapy and surgical treatment, still have the lowest survival rate. Experimental stages using nanoenzymes/nanozymes for ovarian cancer diagnosis and treatment are being carried out, and correspondingly the current treatment approaches to treat breast cancer have a lot of adverse side effects, which is the reason why researchers and scientists are looking for new strategies with less side effects. Nanoenzymes have intrinsic enzyme-like activities and can reduce the shortcomings of naturally occurring enzymes due to the ease of storage, high stability, less expensive, and enhanced efficiency. In this review, we have discussed various ways in which nanoenzymes are being used to diagnose and treat breast and ovarian cancer. For breast cancer, nanoenzymes and their multi-enzymatic properties can control the level of reactive oxygen species (ROS) in cells or tissues, for example, oxidase (OXD) and peroxidase (POD) activity can be used to generate ROS, while catalase (CAT) or superoxide dismutase (SOD) activity can scavenge ROS. In the case of ovarian cancer, most commonly nanoceria is being investigated, and also when folic acid is combined with nanoceria there are additional advantages like inhibition of beta galactosidase. Nanocarriers are also used to deliver small interfering RNA that are effective in cancer treatment. Studies have shown that iron oxide nanoparticles are actively being used for drug delivery, similarly ferritin carriers are used for the delivery of nanozymes. Hypoxia is a major factor in ovarian cancer, therefore MnO2-based nanozymes are being used as a therapy. For cancer diagnosis and screening, nanozymes are being used in sonodynamic cancer therapy for cancer diagnosis and screening, whereas biomedical imaging and folic acid gold particles are also being used for image guided treatments. Nanozyme biosensors have been developed to detect ovarian cancer. This review article summarizes a detailed insight into breast and ovarian cancers in light of nanozymes-based diagnostic and therapeutic approaches.

PRIVACY-AWARE MALARIA DETECTION: U-NET MODEL WITH K-ANONYMITY FOR CONFIDENTIAL IMAGE ANALYSIS

Malaria detection through cell image analysis is essential for early diagnosis and effective treatment, as timely detection can significantly reduce the risk of severe health complications. However, this process raises substantial privacy concerns due to the sensitivity of medical data. This study presents a U-Net model combined with k-anonymity to enhance data security while maintaining high accuracy. The model features a custom Spatial Attention mechanism for improved segmentation performance and incorporates advanced techniques to focus on critical image features. K-Anonymity adds controlled noise to protect data privacy by obfuscating sensitive information. The model achieved a validation accuracy of 99.60%, a Dice score of 99.61%, precision of 99.42%, recall of 99.96%, and an F1 score of 99.69% on malaria cell images. When applied to the Cactus dataset, a real dataset, in agriculture, it achieved an accuracy of 98.58%, an F1 Score of 98.44%, a Dice Score of 95.08%, a Precision of 98.04%, and a Recall of 98.86%, demonstrating its strong generalization capability. These results highlight the effectiveness of integrating privacy-preserving techniques with advanced neural network architectures, improving both security and performance in diverse image analysis applications.

Prognostic impact of matrix metalloproteinase 2 (MMP-2) and matrix metalloproteinase 9 (MMP-9) in Egyptian breast cancer patients.

This study aims to investigate the diagnostic and prognostic relevance of MMP-2 and MMP-9 as biomarkers for breast cancer, as well as their association with clinicopathological factors. Breast cancer is a leading contributor to cancer-related deaths among women worldwide. The discovery of biomarkers is crucial for early diagnosis, outcome prediction, and effective treatment. Matrix metalloproteinases (MMPs) play a significant role in various physiological and pathological activities, including development, tissue repair, inflammation, cancer spread, and metastasis. While the prognostic significance of MMP-2 and MMP-9 levels in breast cancer has been studied, the findings remain inconclusive. Participants were divided into three groups, with each group consisting of 62 individuals: Group I comprised healthy controls, Group II consisted of newly diagnosed breast cancer patients (stage I-III), and Group III included patients with metastatic breast cancer. Levels of MMP-2 and MMP-9 were evaluated in these groups using the ELISA method. An evident increase in MMP-2 and MMP-9 levels was noted when comparing the control group with both the breast cancer and metastatic groups. Furthermore, a notable correlation was identified between serum MMP-9 levels and the pathological diagnosis of breast cancer (P < 0.001) as well as tumor size (P < 0.01). MMP-2 and MMP-9 have emerged as promising biomarkers for breast cancer, with MMP-9 specifically associated with disease prognosis. Continued investigation into the anti-tumor mechanisms of MMPs may yield significant advancements in the development of targeted therapeutic strategies for the management of breast cancer.

Enhancing Tomato Leaf Disease Detection through Generative Adversarial Networks and Genetic Algorithm based Convolutional Neural Network

In the agricultural sector, tomato leaf diseases signify a lot because they result in a lower crop yield and quality. Timely detection and classification of diseases help to ensure early interventions and effective treatment solutions. Nonetheless, the existing methods are confined by the dataset imbalance which affects class distribution negatively and thus results in poor models, especially for rare diseases. The research is designed to improve the capability of tomato leaf disease identification by investing a new deep-learning method beyond the challenge of imbalanced class distribution. By balancing the dataset, we aim to improve classification accuracy as we pay more attention to the under-represented classes. The proposed GAN-based method that combines the Weighted Loss Function to produce tomato leaf disease synthetic images is underrepresented. They improve the quality of the entire dataset, and the images from every class are now in a more balanced proportion. A CNN, which is the convolutional neural network, is trained for the classifier, with the weighted loss function as a part of the model. We used Genetic Algorithm (GA) for hyperparameter optimization of the CNN. It helps in emphasizing the learning process from the under-represented class. The suggested one will not only decrease the accuracy of tomato leaf disease detection but also increase it. Therefore, the synthetic images created by GAN enhance the dataset since the class distribution is brought to equilibrium. The incorporation of the weighted loss function into the model’s training process makes it very effective in handling with the class instability problem and consequently, the model can identify both common and rare diseases. From the outcomes of this study, it can be concluded that it is feasible to employ GAN and one loser weights function to solve the problem of class imbalance in tomato leaf disease recognition. A suggested approach that increases the model’s accuracy and reliability could be a good move to enhancing a reliable method of disease detection in the agricultural sector.