Screening Techniques Research Articles

Fabrication of versatile Fe3O4/GO/Au composite nanomaterial as SERS-active substrate for detection of pesticide residue.

Highly active Fe3O4/GO/Au composite nanomaterial was fabricated as a substrate of surface-enhanced Raman spectroscopy (SERS) and applied for pesticide residue detection. The three-layer multifunctional Fe3O4/GO/Au nanoparticles (NPs) were designed by facile method, with high hotspots, and were characterized by various techniques, including ultraviolet spectrophotometry (UV), X-ray diffraction (XRD), infrared absorption spectrometer (IR), and transmission electron microscopy (TEM). The performance of Fe3O4/GO/Au was evaluated by Raman spectroscopy with R6G as a probe molecule to verify its enhancement effect. It exhibited a strong Raman signal with 10-6M of R6G. Furthermore, the presence of Fe3O4/GO/Au nanohybrid enabled the SERS-based method to detect mancozeb and showed an excellent linear relationship in the range of 0.25-25 ppm, with a low limit of detection (0.077 ppm), satisfactory EF, stability, and repeatability. In addition, the mechanism of SERS enhancement with electromagnetic mechanism (EM) and chemical mechanism (CM) was discussed in detail. Therefore, the proposed SERS approach holds promise as an auxiliary technique for screening contaminated agricultural products, environmental sample, and food in the future.

Analysis of mcr family of colistin resistance genes in Gram-negative isolates from a tertiary care hospital in India.

Colistin serves as the drug of last resort for combating numerous multidrug-resistant (MDR) Gram-negative infections. Its efficacy is hampered by the prevalent issue of colistin resistance, which severely limits treatment options for critically ill patients. Identifying resistance genes is crucial for controlling resistance spread, with horizontal gene transfer being the primary mechanism among bacteria. This study aimed to assess the prevalence of plasmid-mediated mcr genes associated with colistin resistance in Gram-negative bacteria, utilizing both genotypic and phenotypic tests. The clinical isolates (n=913) were obtained from a tertiary care center in Chennai, India. Colistin resistance was seen among Gram-negative isolates. These strains underwent screening for mcr-1, mcr-3, mcr-4, and mcr-5 genes via conventional PCR. Additionally, mcr-positive isolates were confirmed through Sanger sequencing and phenotypic testing. The bacterial isolates predominantly comprised Klebsiella pneumoniae (62.43%), Escherichia coli (19.71%), Pseudomonas aeruginosa (10.73%), and Acinetobacter baumannii (4.81%), along with other species. All isolates exhibited multidrug resistance to three or more antibiotic classes. Colistin resistance, determined via broth microdilution (BMD) using CLSI guidelines, was observed in 13.08% of the isolates studied. Notably, mcr-5 was detected in K. pneumoniae in PCR, despite its absence in Sanger sequencing and phenotypic tests (including the combined-disk test, colistin MIC in the presence of EDTA, and Zeta potential assays). This finding underscores the importance of employing multiple diagnostic approaches to accurately identify colistin resistance mechanisms.

Coming Attraction: The Event of Color, Techniques of Screening and Filtering in Early “Natural” Color Film and Photography

Coming Attraction: The Event of Color, Techniques of Screening and Filtering in Early “Natural” Color Film and Photography

New Horizons in Diabetic Neuropathies: An Updated Review on their Pathology, Diagnosis, Mechanism, Screening Techniques, Pharmacological, and Future Approaches.

One of the largest problems for global public health is diabetes mellitus (DM) and its micro and macrovascular consequences. Although prevention, diagnosis, and treatment have generally improved, its incidence is predicted to keep rising over the coming years. Due to the intricacy of the molecular mechanisms, which include inflammation, oxidative stress, and angiogenesis, among others, discovering treatments to stop or slow the course of diabetic complications is still a current unmet need. The pathogenesis and development of diabetic neuropathies may be explained by a wide variety of molecular pathways, hexosamine pathways, such as MAPK pathway, PARP pathway, oxidative stress pathway polyol (sorbitol) pathway, cyclooxygenase pathway, and lipoxygenase pathway. Although diabetic neuropathies can be treated symptomatically, there are limited options for treating the underlying cause. Various pathways and screening models involved in diabetic neuropathies are discussed, along with their possible outcomes. Moreover, both medicinal and non-medical approaches to therapy are also explored. This study highlights the probable involvement of several processes and pathways in the establishment of diabetic neuropathies and presents in-depth knowledge of new therapeutic approaches intended to stop, delay, or reverse different types of diabetic complications.

Overview of prevalence, etiology, clinical features, diagnosis, and management of primary ovarian insufficiency

Primary ovarian insufficiency (POI) is the development of primary hypogonadism before the age of 40 years in women with normal karyotypes. Patients present with symptoms that are similar to those of menopause, including irregular periods, reduced fertility, and climacteric symptoms due to hypoestrogenism, such as hot flashes, vaginal dryness, dyspareunia, irritability, and night sweats. The incidence of spontaneous POI is approximately 1 in 250 by 35 years of age and 1 in 100 by 40 years of age. Genetic, inflammatory, metabolic, autoimmune, and idiopathic factors contribute to POI. POI pathophysiology is explained by the presence of fewer primordial follicles and their rapid degeneration. Detailed history and examination are important for diagnosis. Diagnosis requires biochemical evidence and laboratory testing, including measurements of follicle-stimulating hormone, thyroid-stimulating hormone, prolactin, and estradiol (E2) levels. In addition, transvaginal ultrasound is preferred to evaluate ovarian reserve. POI should be managed through a multidisciplinary approach involving gynecologists, endocrinologists, and psychologists. Hormonal replacement therapy is the mainstay treatment for all women with POI. Lifestyle modifications such as weight-bearing exercises, smoking cessation, and Vitamin D and calcium supplementation are highly recommended to improve bone density and reduce fractures. Psychological care has been suggested to improve self-esteem and social support, along with family planning guidance. Despite recent discoveries, additional screening techniques are needed.

Longitudinal Analysis of Diagnostic Accuracy of Panoramic Radiography in Oral Pathology

ABSTRACT Objective: The purpose of this 5-year longitudinal study was to assess the diagnostic accuracy of panoramic radiography (PR) in identifying oral pathology at a tertiary care facility. Methods: A retrospective review of patient files was done, encompassing those of patients who had PR for suspected oral lesions, followed by a biopsy. At various times, several diagnostic accuracy metrics were computed, such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic odds ratio (DOR). Results: The research had 250 patients in total. In terms of identifying oral pathology, PR showed a sensitivity of 85% and a specificity of 90%. There were 80% and 92% PPV and NPV, respectively. With a computed DOR of 25, PR’s total discriminating power was found to be high. Conclusion: In summary, PR consistently demonstrates great diagnostic accuracy in identifying oral pathology at a tertiary care facility. These results confirm that PR is a useful screening technique for oral lesions in standard clinical practice. In order to overcome PR’s shortcomings and investigate methods for enhancing its diagnostic effectiveness, more investigation is necessary.

Candida auris cluster in a center with no previous infections associated with a single organ donor

Background: Candida auris is an opportunistic pathogen reported in the US since 2016. C. auris infections (CAI) are frequently healthcare-associated, but only one case of donor-derived CAI in a lung transplant recipient has been reported (PMID 28520901). We describe a cluster of two CAIs at a single center in South Carolina occurring in 2 different recipients from the same solid organ transplant donor. Methods: We describe two cases of invasive CAIs occurring in an academic medical center without prior CAI in Charleston, SC in October 2023. C. auris was identified using Bruker MALDI-TOF and confirmed by the state health department. Results: Patient 1: 40-49 year-old male underwent heart transplantation on day 19 from admission complicated by growth of C. auris on post-op day #15 from a drain. He was readmitted post-op days 22-63 with positive blood cultures for C. auris and underwent re-operation with debridement and hardware removal. C. auris pericarditis required multiple returns to the OR (Figure). He was discharged on micafungin/posaconazole with plans for long-term antifungal therapy. Patient 2: 50-59 year-old male underwent liver and kidney transplantation on day 25 from admission from the same donor as Patient 1 in a separate hospital complex. His course was complicated by possible infected biloma not amendable to drainage and C. auris/C. glabrata fungemia, which was further complicated by abdominal wall collection cultures growing C. auris on post-operative day 35 on washout. He was managed with dual micafungin/posaconazole however, he died of unrelated causes at 93 days after transplant (Figure). Investigation: The donor for both recipients was a 10-19 year-old male who suffered brain death after trauma and was hospitalized for 56 days prior to procurement in Atlanta, GA without known CAI. Airway cultures at the time of organ procurement were positive for rare Pseudomonas and light unidentified yeast of multiple morphologies; urine cultures also grew 40,000 cfu/ml un-identified yeast. Screening of 35 and 4 inpatients in units exposed to patients 1 & 2, respectively, with axilla/groin PCR was negative (Figure). A third organ recipient for this donor (kidney) at our center was identified and had negative urine fungus cultures. Conclusions: Despite no definitive link to a known donor infection, this cluster of CAI occurring simultaneously in 2 patients in separate hospitals/units at a single center with no known prior cases represents likely donor-derived CAI. Our experience suggests that organ procurement organizations should consider improved screening techniques for C. auris in donor cultures.Disclosure: Scott Curry: Consultant- Ferring; Abbott Diagnostics- Speaker honorarium

Bone Marrow Aspiration and Biopsy Findings in Patients with Hematological Disorders in a Tertiary Care Center from Eastern Nepal: An Institutional Experience

Background: Bone marrow study is an important study which is performed when the peripheral blood examination and other laboratory tests and clinical signs and symptoms are suggestive and sometimes inconclusive of both hematological neoplastic and non-neoplastic conditions. The aim of this study was to study about the indications of bone marrow to assess the diagnostic value and comparison of microscopic findings with immunophenotyping and molecular studies. Methods: A total of 102cases were analyzed retrospectively and prospectively from January 2022 to October 2023. All the bone marrow aspiration and trephine biopsy received at the laboratory during this period were included in the study and other cases were analyzed form medical record of the department. Results: Among the 102 cases, mean age of the patient was 48 years and majority were male patients. The age range was between 17-80 years. The male: female ratio was 1.25:1. Weakness was most common presenting complain. Acute leukemia was the most frequent diagnosis (24.5%) followed by myeloproliferative neoplasm (7.8%), primary aplastic anemia (6.9%), myelodysplastic syndrome (5.8%), megaloblastic anemia (4.9%) and plasma cell dyscrasia (3.9%), CLPD (2.9%), ITP (1.9%). Reactive marrow constituted 3.9% and few of the rare diagnosis included hypereosinophilia with PDGFRA mutation, Leishmaniasis etc. Conclusions: The study concludes the bone marrow aspirate and biopsy examination is still the gold standard technique for screening and definite diagnosis of hematological conditions. Immunophenotyping and molecular studies are important for treatment purpose and in case of cytomorphological dilemma.

Qualitative and Quantitative Analysis of the Phytochemicals in Aerva lanata

The current study's objective was to use both qualitative and quantitative screening techniques to determine whether phytochemicals were present in the methanolic, ethanolic, and aqueous extracts of Aerva lanata. Standard techniques were used to analyze the phytochemical compounds present in crude extracts, including steroids, terpenoids, phlobatannins, reducing sugar, triterpenoids, alkaloids, phenolic compounds, flavonoids, saponins, quinones, oxalates, tannins, cardiac glycosides, and anthraquinones. The methanolic extracts showed positive results for the maximum number of phytochemical compounds. In quantitative analysis, the essential secondary metabolites such as phenolic compounds, flavonoids, tannins, carbohydrates, alkaloids, saponins, phenols, and terpenoids were tested in all three solvent extracts of the leaf. The methanolic extract of the leaf showed the highest amounts of phytochemicals when compared with other solvent extracts.

An Update on Non-Invasive Skin Imaging Techniques in Actinic Keratosis-A Narrative Review.

Nonmelanocytic skin cancers (NMSCs) are currently the most common group of human cancers and include all tumors that are not melanomas. Increased exposure to sunlight over the past few years, the lack of regular and proper use of sunscreen, the aging of the population, and better screening techniques are the reasons for the escalation in their diagnosis. Squamous cell carcinoma (SCC) comprises nearly 37% of the tumors in this group and can originate from actinic keratosis (AK), which usually presents as pink, often scaly plaques, usually located on the face or scalp. Advances in dermatoscopy, as well as the development of other non-invasive skin imaging modalities such as high-frequency ultrasound (HFUS), reflectance confocal microscopy (RCM), and optical coherence tomography (OCT), have allowed for greatly increased sensitivity in diagnosing these lesions and monitoring their treatment. Since AK therapy is usually local, and SCCs must be removed surgically, non-invasive imaging methods enable to correctly qualify difficult lesions. This is especially important given that they are very often located on the face, and achieving an appropriate cosmetic result after treatments in this area is very important for the patients. In this review, the authors describe the use of non-invasive skin imaging methods in the diagnosis of actinic keratosis.

Fluorescence excitation-scanning hyperspectral imaging with scalable 2D–3D deep learning framework for colorectal cancer detection

Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians.

Understanding Prostate Cancer: Causes, Risks, Diagnosis, and Treatment

Prostate cancer is the most common cancer among men after skin cancer. Most men who get prostate cancer don't die from it. More than 90% of men diagnosed with prostate cancer survive for at least five years. If detected early, while the tumor has not spread outside of the prostate, the survival rate is nearly 100% with treatment. Early-stage prostate cancer rarely causes symptoms, symptoms develop after the disease progresses. Prostate-specific antigen (PSA) blood test and digital rectal exam (DRE) are common screening methods for prostate cancer. However, whether or not to use these screening techniques is still controversial. Active surveillance is a strategy for managing low-risk prostate cancer without immediate treatment. The idea is to avoid unnecessary side effects by carefully watching how the cancer develops over time.

DEVELOPMENT OF A REFERENCE BOOK BASED ON THE IDENTIFICATION DATA OF MEDICINAL PLANTS IN THE COMMUNITY OF THE SULTANATE OF MOLOKU KIERAHA, NORTH MALUKU, INDONESIA

The aim of this research is to develop teaching materials in the form of a reference book based on studies of plant medicinal identification in the people of the Moloku Kieraha sultanate. The research involved students in semester 2 of the Unkhair Postgraduate Biology Education program for the 2023/2024 academic year. The study used interview sheets, observation sheets, validation sheets, and response questionnaires for data collection, and validation sheet and product trial analysis for analysis. The results of the research identifies 36 medicinal plants used by the Serawai tribe community in Torano Village, Central Ternate District, Maluku Province. Phytochemical analysis reveals that these plants contain secondary metabolite compounds, including tannins, alkaloids, saponins, flavonoids, terpenoids, and steroids. Students showed a positive response to the medicinal plant reference book that had been developed, with an interest level reaching 98%. Therefore, teaching materials are needed to guide students in conducting phytochemical tests to determine the content, benefits, and techniques of phytochemical screening in natural materials.

A novel insight into neurological disorders through HDAC6 protein–protein interactions

Due to its involvement in physiological and pathological processes, histone deacetylase 6 (HDAC6) is considered a promising pharmaceutical target for several neurological manifestations. However, the exact regulatory role of HDAC6 in the central nervous system (CNS) is still not fully understood. Hence, using a semi-automated literature screening technique, we systematically collected HDAC6-protein interactions that are experimentally validated and reported in the CNS. The resulting HDAC6 network encompassed 115 HDAC6-protein interactions divided over five subnetworks: (de)acetylation, phosphorylation, protein complexes, regulatory, and aggresome-autophagy subnetworks. In addition, 132 indirect interactions identified through HDAC6 inhibition were collected and categorized. Finally, to display the application of our HDAC6 network, we mapped transcriptomics data of Alzheimer’s disease, Parkinson’s disease, and Amyotrophic Lateral Sclerosis on the network and highlighted that in the case of Alzheimer’s disease, alterations predominantly affect the HDAC6 phosphorylation subnetwork, whereas differential expression within the deacetylation subnetwork is observed across all three neurological disorders. In conclusion, the HDAC6 network created in the present study is a novel and valuable resource for the understanding of the HDAC6 regulatory mechanisms, thereby providing a framework for the integration and interpretation of omics data from neurological disorders and pharmacodynamic assessments.

The challenges and breakthroughs in the development of diagnostic monoclonal antibodies

AbstractOver the past century, the field of antibody discovery has undergone significant evolution, excluding the current exploration stage of artificial intelligence‐based antibody generation and the often overlooked non‐animal sourced antibody discovery, which typically requires mature in vitro affinity and the selection of high‐quality antigen formulations. This journey has traversed various stages, from methods involving serum‐based antibody acquisition, the isolation of B cells capable of perpetual antibody production through hybridoma technology, to the in‐depth exploration of genetic material using the phage display system, and the current stage involving diverse single B cell screening techniques. Additionally, the emergence of machine learning has brought impressive scientific and technological breakthroughs across research domains, proving to be a powerful application in the field of antibody discovery. However, each technique comes with its limitations, such as variability and control challenges in serum‐based acquisition, lengthy and difficult hybridoma‐derived antibody development, potential limitations in sequence and epitope diversity due to immunization biases in phage display techniques, and costly single B cell screening. Protein mass spectrometry sequencing, with shorter acquisition time and lower costs, is seen as a shortcut by diagnostic companies, impacting traditional antibody development. In diagnostic antibody development, methodological differences in downstream assays and the impact of constant regions outside the Fv core are often neglected. This paper deeply analyzes challenges, proposing innovative strategies for the next generation of diagnostic antibody development. Aimed at moving closer to the gold standard of antibody discovery, these strategies enhance the competitiveness of diagnostic reagent products.

Learning Causes of Functional Dynamic Targets: Screening and Local Methods.

This paper addresses the challenge of identifying causes for functional dynamic targets, which are functions of various variables over time. We develop screening and local learning methods to learn the direct causes of the target, as well as all indirect causes up to a given distance. We first discuss the modeling of the functional dynamic target. Then, we propose a screening method to select the variables that are significantly correlated with the target. On this basis, we introduce an algorithm that combines screening and structural learning techniques to uncover the causal structure among the target and its causes. To tackle the distance effect, where long causal paths weaken correlation, we propose a local method to discover the direct causes of the target in these significant variables and further sequentially find all indirect causes up to a given distance. We show theoretically that our proposed methods can learn the causes correctly under some regular assumptions. Experiments based on synthetic data also show that the proposed methods perform well in learning the causes of the target.

Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study.

The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This is especially true for inexperienced doctors. To improve accuracy, a computer-assisted diagnosis system is used for more effective pneumoconiosis diagnoses. Three models (Resnet50, Resnet101, and DenseNet) were used for pneumoconiosis classification based on 1250 chest X-ray images. Three experienced and highly qualified physicians read the collected digital radiography images and classified them from category 0 to category III in a double-blinded manner. The results of the 3 physicians in agreement were considered the relative gold standards. Subsequently, 3 models were used to train and test these images and their performance was evaluated using multi-class classification metrics. We used kappa values and accuracy to evaluate the consistency and reliability of the optimal model with clinical typing. The results showed that ResNet101 was the optimal model among the 3 convolutional neural networks. The AUC of ResNet101 was 1.0, 0.9, 0.89, and 0.94 for detecting pneumoconiosis categories 0, I, II, and III, respectively. The micro-average and macro-average mean AUC values were 0.93 and 0.94, respectively. The accuracy and Kappa values of ResNet101 were 0.72 and 0.7111 for quadruple classification and 0.98 and 0.955 for dichotomous classification, respectively, compared with the relative standard classification of the clinic. This study develops a deep learning based model for screening and staging of pneumoconiosis is using chest radiographs. The ResNet101 model performed relatively better in classifying pneumoconiosis than radiologists. The dichotomous classification displayed outstanding performance, thereby indicating the feasibility of deep learning techniques in pneumoconiosis screening.

Machine Learning Techniques in Enhanced Oil Recovery Screening Using Semisupervised Label Propagation

Summary Efficiently choosing the optimal enhanced oil recovery (EOR) technique is a critical requirement in reservoir engineering. Machine learning (ML) methods, with a well-established history of application, serve as a swift and dependable tool for EOR screening. In this paper, we aim to evaluate the effectiveness of various ML algorithms for EOR screening, utilizing a comprehensive database of nearly 1,000 EOR projects. This study delves into a comprehensive evaluation of regression and classification-based algorithms to develop a reliable screening system for EOR predictions and address challenges such as limited labeled data and missing values. Our analysis considered various EOR processes, including gas injection, chemical, and thermal EOR techniques. Various ML methods such as random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), shallow artificial neural networks (SANN), naive Bayes classifier (NBC), logistic regression (LR), and decision tree (DT) are applied, enabling both intermethod comparisons and evaluations against advanced methods, multiobjective deep artificial neural networks (MDANN), and multiobjective artificial neural networks (MANN). These advanced techniques provide the unique capability to concurrently address both regression and classification tasks. Considering that conventional methods can only be implemented on a single task, the RF, MANN, MDANN, and KNN algorithms demonstrated top-tier performance in our classification analysis. Regarding the regression task, KNN, RF, and MDANN displayed exceptional performance, signifying their prowess in predictive accuracy. However, MANN exhibited moderate performance in regression analysis. In addition, our study identified areas where certain algorithms, such as support vector regression (SVR), exhibited weaker performance, highlighting the importance of comprehensive model evaluation. This paper contributes novel insights into the application of ML techniques for EOR screening in the petroleum industry. By addressing challenges such as limited labeled data and missing values and by providing a thorough evaluation of various ML algorithms, our study offers valuable information for decision-makers in the oil and gas sector, aiding in the selection of suitable algorithms for EOR projects. In addition, the use of semisupervised label propagation and advanced techniques like KNN imputation adds to the existing body of literature, enhancing the state of knowledge in this domain.

Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection

This study introduces an innovative data-driven and machine-learning framework designed to accurately predict site scores in the site screening study for specific offshore CO2 storage sites. The framework seamlessly integrates diverse sub-surface geospatial data sources with human aided expert-weighted criteria, thereby providing a high-resolution screening tool. Tailored to accommodate varying data accessibility and the significance of criteria, this approach considers both technical and non-technical factors. Its purpose is to facilitate the identification of priority locations for projects associated with Carbon Capture, Utilization, and Storage (CCUS). Through aggregating and analyzing geospatial datasets, the study employs machine learning algorithms and an expert-weighted model to identify suitable geologic CCUS regions. This process adheres to stringent safety, risk control, and environmental guidelines, addressing situations where human analysis may fail to recognize patterns and provide detailed insights in suitable site screening techniques. The primary emphasis of this research is to bridge the gap between scientific inquiry and practical application, facilitating informed decision-making in the implementation of CCUS projects. Rigorous assessments encompassing geological, oceanographic, and eco-sensitivity metrics contribute valuable insights for policymakers and industry leaders. To ensure the accuracy, efficiency, and scalability of the established offshore CO2 storage facilities, the proposed machine learning approach undergoes benchmarking. This comprehensive evaluation includes the utilization of machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Extreme Learning Machine (MLELM), and Deep Neural Network (DNN) for predicting more suitable site scores. Among these algorithms, the DNN algorithm emerges as the most effective in site score prediction. The strengths of the DNN algorithm encompass nonlinear modeling, feature learning, scale invariance, handling high-dimensional data, end-to-end learning, transfer learning, representation learning, and parallel processing. The evaluation results of the DNN algorithm demonstrate high accuracy in the testing subset, with values of AAPD (Average Absolute Percentage Difference) = 1.486 %, WAAPD (Weighted Average Absolute Percentage Difference) = 0.0149 %, VAF (Variance Accounted For) = 0.9937, RMSE (Root Mean Square Error) = 0.9279, RSR (Root Sum of Squares Residuals) = 0.0068, and R2 (Coefficient of Determination) = 0.9937.

Enhancing Crop Resilience: Advances and Challenges in Marker-Assisted Selection for Disease Resistance

Marker-assisted selection (MAS) has emerged as a pivotal technique in crop improvement programs, enabling the identification and incorporation of disease-resistant traits in crops. This review highlights the significance of MAS in enhancing disease resistance, the current state of research, and the challenges faced in its implementation. Various case studies, including successful applications in crops such as rice, wheat, and maize, demonstrate the potential of MAS in sustainable agriculture. The review also discusses the integration of MAS with other modern breeding techniques and emphasizes the need for advanced genomic tools for future advancements in this field. The development of advanced molecular markers, high-throughput screening techniques, and the incorporation of genomic selection approaches have all contributed to recent developments in MAS. Even in intricate polygenic situations, these developments have significantly improved t+he precision and speed of discovering disease-resistant characteristics. Case studies involving important crops such as maize, wheat, and rice show how MAS can be successfully applied to create varieties with improved resistance to certain diseases, significantly lowering crop losses and reduced use of pesticides. These developments have consequences that go beyond the purely technical; they could have a big influence on environmentally friendly agriculture methods, cost effectiveness, and sustainable development. This study addresses the possible future path of MAS in crop breeding and summarizes the major discoveries from current research, highlighting both the successes and difficulties in the field. The body of data highlights MAS as a critical instrument in the toolbox of the contemporary breeder, essential for satisfying the ever-increasing demands of a world that is changing quickly.