Screening Techniques Research Articles

Plant Species with Antimicrobial Effects and the Importance of a Standardization of Protocols for Antimicrobial Agents

In modern times, microbial resistance is a global threat to health and development. The misuse and inappropriate use of antimicrobials is the main cause of developing drugresistant pathogens. It requires multiple areas in direction to attain sustainable advance goals. As a result of microbial resistance, the necessity for costly medications and expenses are obstructed worldwide. Due to rising attention in the research of new antimicrobial medicaments from a variety of natural or synthetic sources to fight microbial resistance. Thus, natural antimicrobial agents have been used to a great extent nowadays because plant-derived antimicrobial agents are considered to be safer alternatives for health as compared to those synthetic antimicrobial agents. Overall, the active ingredients, water, essential oils, and ethanolic extracts from selected plants and the mixture of a variety of these natural extracts have been used for centuries, because they possess antimicrobial activity which inhibits the growth of microbes. Natural plants as an antimicrobial agent, like extracts of Curcuma longa L., Piper nigrum L., Vachellia leucophloea, Eclipta prostrata, Ocimum sanctum L., Terminalia arjuna, Manihot esculenta Crantz, Lawsonia inermis L., Zingiber officinale Roscoe, Camellia sinensis (L.) Kuntze, Coriandrum sativum L., Carica papaya L., Cinnamomum tamala, and many others have been preferred and used for ages because they are easily available worldwide. They are usually of low cost and have little or no side effects. Several antimicrobial screening approaches like the disk-diffusion method, well diffusion method, micro broth dilution assay, sterile disk method, and agar diffusion method are generally cast off for measurement of reproducibility and standardization of these antimicrobial agents. This review article is a comprehensive description of natural plants like Coriandrum sativum L., Carica papaya L., Cinnamomum tamala, etc., containing those extracts used as antimicrobial agents listed, and numerous in vitro antimicrobial susceptibility testing methods are reported. These identified plant species and antimicrobial screening techniques hold the potential for formulating these plants into antimicrobial drugs, warranting further study and exploration in the field of medicine.

Development of rapid, efficient and cost effective screening technique for testing arecanut against Phytophthora meadii incitant of fruit rot disease

To accelerate identification of disease resistant arecanut germplasm or hybrids against Phytophthora, it is very much imperative to develop bioassays which could differentiate resistant and susceptible cultivars efficiently. Here, developed a cost effective and rapid technique called “Detached Leaf Assay” to identify resistant germplasm at the seedling stage itself. Standardized zoospore production in highly virulent Phytophthora meadii (P19) by incubating under 12 hours light and dark regime. Zoospore suspension adjusted to 105spores ml-1 in petri plates. Subsequently, surface sterilized arecanut leaves were floated in zoospore suspension and incubated at temperature of 24±1°C. Disease symptoms, including water-soaked lesions, were recorded three days after inoculation. Infection lesion increased from 1 to 7.3 cm2. The pathogen was re-isolated and confirmed with the original culture. The assay was successfully validated to screen arecanut accessions, wild types and hybrids against P. meadii. This technique is the first to be developed, and it is simple, cost-effective, and faster. It also provides consistent infection and could be effectively utilized to screen arecanut germplasm or hybrids against P. meadii in the seedling stage itself.• Developed a cost effective, efficient and rapid screening technique• The technique was validated to identify resistant genotypes against Phytophthora meadii at the seedling stage.

Discovery of the synergy between sulindac and NO expands the application of NSAIDs in cardiac complications

Cardiac inflammation, a pathological cornerstone in cardiac dysfunction, triggers diastolic impairment, leading to profound myocardial hypoxia. This hypoxic state serves as a potent stimulus for the amplification of inflammatory mediator release, ultimately limiting the therapeutic potential of monotherapies. To address this challenge, compounds that integrate non-steroidal anti-inflammatory drugs (NSAIDs) with nitric oxide (NO) donors was synthesized. The in vitro screening unveiled the remarkable anti-hypoxic inflammatory injury activity of a sulindac derivative, despite its suboptimal COX-2 inhibition when comparing with other NSAIDs derivatives. This revelation propelled us to delve deeper into the intricate synergistic relationship between sulindac and NO. Employing two-way ANOVA and high-throughput screening technique, we incontrovertibly confirmed their synergism. Further, the underlying mechanism was unmasked through the detection of signal molecule release levels and western blot analysis. In a definitive step, we evaluated the therapeutic effects of sulindac and its derivatives in vivo, leveraging a rat model that recapitulates cardiac inflammation coupled with hypoxia. Our findings herald a promising drug candidate and establish a foundation for the expanded utilization of NSAIDs in the intricate landscape of cardiovascular diseases.

Enhancing precision nitrogen management for cotton cultivation in arid environments using remote sensing techniques

Context or problemRemote sensing, particularly through unmanned aerial vehicles (UAVs), has emerged as a pivotal tool in precision agriculture, especially for nitrogen (N) management. Traditional methods, while effective in quantifying crop N status using the Nitrogen Nutrition Index (NNI), fall short in providing quantitative fertilization strategies. MethodsThis study bridges this gap by developing a comprehensive method that leverages multispectral remote sensing data from UAVs to refine N fertilizer management in cotton cultivation within arid environments. By integrating both field observations and UAV-derived multispectral data, we established robust models capable of estimating both leaf and overall cotton nitrogen contents (CNC-leaf and CNC-all), as well as NNI, throughout the growing season. This facilitated real-time calculation of required N fertilizer doses in cotton fields. ResultsWe uniquely applied covariance diagnosis and full subset screening techniques, underscoring the efficacy of vegetation index categories (VIs) in enhancing prediction accuracy. The Random Forest (RF) model exhibited superior performance in predicting plant nitrogen content, particularly in CNC-leaf prediction (Calibration: R²=0.92, RMSE=7.7 g m⁻², MAE=5.5 g m⁻²; Validation: R²=0.60, RMSE=16.5 g m⁻², MAE=12.2 g m⁻²) as opposed to CNC-all prediction (Calibration: R²=0.78, RMSE=117.0 g m⁻², MAE=154.3 g m⁻²; Validation: R²=0.34, RMSE=138.4 g m⁻², MAE=190.4 g m⁻²). The RF model also demonstrated optimal performance in NNI prediction (Calibration: R²=0.93, RMSE=0.05, MAE=0.04; Validation: R²=0.73, RMSE=0.12, MAE=0.10), surpassing the predictions for CNC. ConclusionsUtilizing CNC and NNI estimates derived from the optimized RF model, this study succeeded in generating a comprehensive map detailing the N fertilizer requirement across cotton Fertilization zones were established for different treatments, revealing that biochar application levels primarily determine nitrogen fertilizer needs. As biochar application increases, nitrogen fertilizer demand decreases. Moreover, nitrogen application rates typically increase when irrigation levels reach either 120 % ETc or 60 % ETc. Implications or significanceThis innovative approach not only empowers farmers with intuitive and accurate tools for real-time cotton N management but also fosters enhanced agricultural practices by integrating advanced remote sensing technologies with sophisticated data analysis methods. The findings of this study have significant implications for sustainable and efficient agricultural practices, particularly in arid regions, setting a new precedent in precision nitrogen management.

Hyperspectral sensing for high‐throughput chloride detection in grapevines

AbstractSoil salinity affects major viticultural areas worldwide with chloride ions being the primary source of salt toxicity in grapevines (Vitis vinifera). This toxicity impacts vine health and reduces fruit yield and quality. Current breeding efforts to improve grapevine salinity tolerance are limited by the low throughput of available phenotyping methods, which are time‐consuming, labor‐intensive, and destructive. This study demonstrated that hyperspectral proximal sensing can be utilized as a high‐throughput, nondestructive screening technique to identify salinity‐tolerant grapevine germplasm. The predictive abilities of two different hyperspectral devices, which varied in price, resolution, and sensitivity, were compared across 23 Vitis accessions spanning eight species. Prediction models were built using hyperspectral reflectance and leaf chloride content measured with a lab chloridometer. Three distinct approaches were studied: (1) analyzing the correlation between individual wavelengths and chloride content; (2) employing machine learning models, including partial least square regression (PLSR), random forest, and support vector machine, utilizing all wavelengths; and (3) classification‐based prediction using partial least square discriminant analysis (PLSDA). Multiple regions in the spectrum, including 613–660 nm, 689–696 nm, and 1357–1358 nm, showed a medium correlation (0.30–0.50) with chloride content in the leaves. PLSR was the most effective machine learning approach, demonstrating moderate predictive capability for chloride content (maximum R2 = 0.67), though performance varied between the two devices tested. With PLSDA, predictions increased considerably, up to an accuracy of 0.97, depending on the instrument used and the spectral data transformation. Overall, the more expensive and sensitive device with a wider spectral range outperformed the more affordable, shorter range device. However, when the prediction model was based on classes (chloride excluders vs. non‐excluders) rather than chloride content, the differences in prediction abilities were minimal, with both instruments performing very well. This is promising for identifying breeding materials with chloride exclusion capabilities at low cost and high throughput.

Empirical Modification of Force Fields for the Development of Peptide‐Based Gas Sensors

AbstractMolecular dynamics models combined with computational approaches can be used as advanced screening techniques for finding highly efficient material‐molecule interactions based on binding affinity, including in the development of gas sensors. However, most models are originally designed for liquid phase interactions, which do not align with gas sensing conditions, resulting in lower‐than‐expected performance. This study introduces an empirical modification method to adjust peptide interaction models for a gas phase, aiming to better accommodate the interaction between pentapeptides and target gas molecules. By adapting the weights of terms in the Gibbs free energy equation given in an empirical force field model, we demonstrate a significant increase in the absolute value of coefficient of determination (R02) , from an average of 0.05 with conventional liquid phase models to 0.90 with proposed gas phase models. An empirical modification technique for gas phase interactions markedly enhances the prediction accuracy of models, facilitating the effective development of peptide‐based gas sensors.

Hepatocellular Carcinoma Surveillance Strategies: Major Guidelines and Screening Advances

Hepatocellular Carcinoma Surveillance Strategies: Major Guidelines and Screening Advances

Review on Histopathological Techniques in Cervical Cancer Screening

Histopathological techniques remain the basis for cervical cancer care by making diagnosis and model treatment options to improve patient outcomes. It involves the examination of cervical tissue samples to establish a diagnosis, grade precancerous lesions, and stage invasive cancer that guide treatment options. We examined published reports and provided updated information on histopathological techniques in cervical cancer screening using the PRISMA guidelines for review. Histopathology examination is used to confirm precancerous lesions and stage invasive cancer. Accumulated data obtained also informs healthcare providers about the severity of the lesions, allowing decisions for personalized treatment plans and prognosis. While cytology screening is an important tool for cervical cancer screening, histopathological techniques are indispensable to confirm the diagnosis and determine appropriate treatments. Therefore, it is essential to continue streamlining the techniques and scoring patterns to guarantee for their reliability in various contexts.

Advanced analytical methods for multi-spectral transmission imaging optimization: enhancing breast tissue heterogeneity detection and tumor screening with hybrid image processing and deep learning.

Light sources exhibit significant absorption and scattering effects during the transmission through biological tissues, posing challenges in identifying heterogeneities in multi-spectral images. This paper introduces a fusion of techniques encompassing the spatial pyramid matching model (SPM), modulation and demodulation (M_D), and frame accumulation (FA). These techniques not only elevate image quality but also augment the precision of heterogeneous classification in multi-spectral transmission images (MTI) within deep learning network models (DLNM). Initially, experiments are designed to capture MTI of phantoms. Subsequently, the images are preprocessed separately through a combination of different techniques such as SPM, M_D and FA. Ultimately, multi-spectral fusion pseudo-color images derived from U-Net semantic segmentation are fed into VGG16/19 and ResNet50/101 networks for heterogeneous classification. Among them, different combinations of SPM, M_D and FA significantly enhance the quality of images, facilitating the extraction of heterogeneous feature information from multi-spectral images. In comparison to the classification accuracy achieved in the original image VGG and ResNet network models, all images after preprocessing effectively improved the classification accuracy of heterogeneities. Following scatter correction, images processed with 3.5 Hz modulation-demodulation combined with frame accumulation (M_D-FA) attain the highest classification accuracy for heterogeneities in the VGG19 and ResNet101 models, achieving accuracies of 95.47% and 98.47%, respectively. In conclusion, this paper utilizes different combinations of SPM, M_D and FA techniques to not only enhance the quality of images but also further improve the accuracy of DLNM in heterogeneous classification, which will promote the clinical application of MTI technique in breast tumor screening.

A quality by design strategy for cocrystal design based on novel computational and experimental screening strategies: part A.

While pharmaceutical Cocrystals have long been acknowledged as a promising method of enhancing a drugs bioavailability, they have not yet experienced widespread industrial adoption on the same scale as other multi-component drugs, such as salts and amorphous solid dispersions. This is partly due to the lack of a being no definitive screening strategy to identify suitable coformers, with the most cocrystal screening strategies heavily relying on trial and error approaches, or through utilizing a multiple and often conflicting, computational screening techniques combined with high material consumption experimental techniques. From the perspective of industry, this can often lead to high material waste and increased costs, encouraging the prioritization of more traditional bioenhancement techniques. Here we present a strategy for the selection of multicomponent systems involving computational modelling for screening of drug- former pairs based on a combination of molecular complementarity and H-bond propensity screening. Jet dispensing printing technology is co-opted as a mechanism for High-Throughput Screening (HTS) of different stoichiometric ratios, as a low material consumption screening strategy. This strategy is presented herein as a Quality by Design (QbD) crystal engineering approach, combined with experimental screening methods to produce cocrystals of a novel 5-Lipoxygenase (5-LO) inhibitor, PF-04191834 (PF4). Through this methodology, three new cocrystals were indicated for PF4, confirmed via DSC and XRPD, from less than 50mg of original testing material. Part B of this study will demonstrate the scalability of this technique continuous extrusion.

Diagnostic performance of dual-energy CT with electron-density reconstruction for lumbar disc herniation

Background: Magnetic resonance imaging (MRI) is used for the evaluation of degenerative spinal disease. However, its utility is restricted in routine practice because of contraindications and a lack of widespread availability. Dual-energy computed tomography (DECT) is a newer technique for the evaluation of degenerative spinal disease.Objectives: This study aimed to evaluate the diagnostic performance of DECT with electron-density (ED) image reconstruction compared to standard CT for the detection of lumbar disc herniation, with MRI as the gold standard.Method: The retrospective study included 84 patients between 01 July 2023 to 31 December 2023 who underwent DECT and 1.5-T MRI within 1 week. Four radiologists, blinded to the clinical and MRI information, independently evaluated the standard CT series and DECT series with ED reconstructions for lumbar disc herniation and spinal nerve root impingement. The gold standard for comparison was lumbar spine MRI, and diagnostic accuracy was measured with sensitivity and specificity.Results: MRI revealed 417 lumbar disc herniations. Dual-energy computed tomography with ED reconstruction showed higher sensitivity (86.36% [532/616] vs. 57.79% [356/616]) and specificity (96.86% [1019/1052] vs. 95.82% [1008/1052]) for the detection of lumbar disc herniation compared to standard CT.Conclusion: Dual-energy computed tomography with ED reconstruction shows better diagnostic performance for the detection of lumbar disc herniation compared to standard CT and can be a useful alternative imaging modality when MRI is contraindicated or unavailable.Contribution: This study shows the usefulness of DECT as an alternative imaging technique for screening of degenerative spinal disease whenever MRI is contraindicated or unavailable.

Synthesis of β-cyclodextrin magnetic-graphene-oxide pyrenyl (β-mGOP) gels for high-throughput enrichment and detection of various trace mycotoxins in food using UHPLC-cIMS

In this research, β-cyclodextrin magnetic-graphene-oxide pyrenyl gel composite (β-mGOP) was employed to remove trace mycotoxins from food products. The β-mGOP gels exhibited a three-dimensional macroporous structure that facilitated the rapid diffusion of target molecules into the nanoporous structure of the covalent organic frameworks embedded within the gel. The adsorption process of mycotoxins onto the β-mGOP gels conformed to the pseudo-second-order kinetics and the Freundlich adsorption model. These findings demonstrated exceptional rapid adsorption kinetics without pore collapse, making the material suitable as fillers for solid-phase extraction adsorbents. Additionally, a novel screening technique utilizing β-mGOP gels in conjunction with cyclic ion mobility mass spectrometry was developed to illustrate the efficacy of β-mGOP gels in capturing various trace substances in food and enhancing detection resolution. The versatile, uncomplicated, and scalable characteristics of β-mGOP gels indicate their promising potential for future applications in high-throughput analysis of trace contaminants in food.

Attributes Required for Entrepreneurs of Self-Help Groups (SHGS) Involved in the Development of Microenterprises

Under the pathetic and uneasy conditions the rural poor face, microfinance activity acts as the saviour to redeem them from the clutches of poverty and unemployment. The present study was conducted to examine the influence of entrepreneurial attributes of members of SHGs in the effectiveness of microfinance services for agro-enterprise development by the Self-Help Groups (SHGs) in Kerala. The study was conducted in ex-post facto design, in the three purposively selected districts of the three zones of Kerala, namely, Thiruvananthapuram, Thrissur and Malappuram. The effectiveness of microfinance services was calculated individually from the scores obtained for each respondent and the average score of members in a group constituted the effectiveness score of the group. The independent samples t-test was employed to find out the significant difference in entrepreneurial characteristics score with the effectiveness of microfinance services. Also, a correlation analysis was performed between entrepreneurial characteristics and the effectiveness of microfinance services. The correlation analysis of the entrepreneurial characteristics of the SHG members and the effectiveness of microfinance services revealed that ‘achievement motivation’, ‘innovativeness’, ‘thrift tendency’, ‘strategic visioning’ and ‘internal locus of control’ were positively and significantly correlated with the effectiveness of microfinance services at one per cent level of significance, whereas ‘economic opportunism’ was positively and significantly correlated with the effectiveness of microfinance services at five per cent level of significance. The independent samples t-test revealed that the entrepreneurial abilities of the members of SHG largely influenced the effectiveness of microfinance services for agro-enterprise development rather than other factors like the nature of agro-enterprises which was usually selected through micro screening techniques. The study necessitates a severe need of the establishment of business schools for youth to develop their entrepreneurial traits as well as better governance, especially in the agro sector.

Accelerated hit identification with target evaluation, deep learning and automated labs: prospective validation in IRAK1

BackgroundTarget identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches.ResultsWe synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, 2 of which represent novel candidates for IRAK1 and hold promise for future development.ConclusionThis study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5’s HydraScreen and Strateos’ automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs.Scientific contributionWe present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery.

The screening and management of skin diseases in remote African regions: a narrative review.

Skin diseases in remote African regions are often exacerbated due to a lack of resources and expertise with little current research addressing the challenges that patients and healthcare systems experience. Although skin diseases in remote African regions are prevalent and pose a substantial health burden, they remain neglected and understudied. This review aims to explore current challenges in the diagnosis and management of skin diseases in African regions and focus on the need for enhanced understanding and improved healthcare strategies. We reviewed papers that were retrieved from PubMed, Google Scholar, and other relevant academic search sites to analyze the prevalence, screening techniques, as well as management strategies and obstacles associated with dermatological diseases in remote African regions. Current literature suggests there is indeed a high prevalence of skin diseases where other infections are predominant. Further, different factors, such as socioeconomic status and environmental conditions, make it challenging for optimal screening and treatment of skin diseases in remote African regions. Common screening methods for skin diseases involve clinical examination and laboratory tests, while management includes pharmacotherapy, surgery, and counseling approaches. Overall, access to medications and adequate healthcare services remains limited. Early diagnosis and management of skin diseases in remote African regions have significant challenges, including a lack of funding for dermatology, education, and research. Future promising strategies such as teledermatology, point-of-care diagnostic technologies, and task shifting show promise in improving access to care. However, there is a need for sustained efforts to address the underlying socioeconomic factors and improve healthcare delivery in remote African regions.

Abstract A016: Early pancreatic cancer detection using extracellular vesicle DNA methylation signatures in blood

Abstract In 2020, there were 495,773 new cases and 466,003 deaths for pancreatic cancer worldwide. Furthermore, pancreatic cancer is emerging to be the leading cause of cancer related deaths in the U.S. by the year 2030, surpassing lung and colon cancer. The majority of pancreatic cancer patients (∼80%) are diagnosed at the most advanced (metastatic) stage, for which the 5-year survival is 3%. Dramatically, studies show that if it can be detected at an early stage when surgical removal is feasible, the 5-year survival rate significantly increased to 37%. This data highlights the crucial importance of early detection for pancreatic cancer treatment with curative intent. Unfortunately, currently there are no reliable techniques for routine screening to identify pancreatic cancer at early stages. In this Abstract, we developed and validated the performance of those DNA methylation markers to detect pancreatic cancer in a cohort of pancreatic cancer patients. Firstly, we collected a large amount of genome-wide methylation microarray data (targeting over 450,000 methylation sites) of tissue samples from several publicly available databases, including samples of solid pancreatic tumors (n = 384), noncancerous pancreatic tissues (n = 126) and healthy whole blood (n = 453). The raw microarray data were preprocessed using a pipeline implemented in R, which removes inaccurate measurements and corrects for bias caused by technical issues. A two-stage feature selection procedure was designed and implemented in Python, which makes use of various filters and a multivariate embedded method, support vector machine recursive feature elimination (SVM-RFE). The features were evaluated using two metrics, classification performance and stability, based on a tradeoff of which we selected a set of differentially methylated loci for discriminating the samples. The set of prospective markers includes 129 hypermethylated and 142 hypomethylated tumor-specific loci, 8 hypermethylated and 8 hypomethylated pancreatic tissue specific loci. To validate the performance of those pancreatic cancer-specific methylation markers, as a feasibility study, we isolated extracellular vesicles from cancer cells and plasma of cancer patients using lipid nanoprobe (LNP). Extracellular vesicles (EVs) are lipid-bilayer-enclosed vesicles of sub- micrometer size that are secreted by virtually all cell types. In a pilot study encompassing 11 pancreatic cancer patients (Stage I-IV) and age-matched 10 healthy donors (HD), we employed quantitative methylation-specific polymerase chain reaction assays (qMSP) to examine the promoter methylation status of the genes within plasma EV DNA. The receiver operating characteristic (ROC) curves were employed to assess the performance of a combined promoter methylation signature in EV DNA for the detection of pancreatic cancer. Our findings indicate that the promoter methylation of genes can be specific to pancreatic cancer, suggesting the efficient packaging of methylation information from genomic DNA into EVs. Citation Format: Hongzhang He, Siyang Zheng. Early pancreatic cancer detection using extracellular vesicle DNA methylation signatures in blood [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A016.

High‐Throughput Screening Technologies of Efficient Catalysts for the Ammonia Economy

AbstractDriven by the growing global demand for sustainable energy, ammonia is gaining recognition as a crucial energy carrier with the potential for widespread manufacturing. This has spurred the emergence of a “sustainable ammonia economy”. Efficient catalyst development is paramount to achieving this purpose. Although the traditional trial‐and‐error methods have led to substantial progress, they are inherently time‐consuming. In recent years, high‐throughput screening strategies are revolutionizing catalyst discovery, offering an efficient and cost‐effective approach for identifying high‐performance catalysts across various processes within the ammonia economy. This review explores recent advancements in these rapid catalyst screening techniques for ammonia‐based processes. By outlining the general screening principles and the key steps involved in the computational analysis of catalysts relevant to different stages of the ammonia economy, this review aims to provide fundamental insights for the development of novel catalysts using these accelerated methods.

Comprehensive overview of host plant resistance and its progress in wilt resistance in castor bean (Ricinus communis L.)

Castor bean, a non-edible monotypic species known for its unique hydroxy fatty acid, is susceptible to a range of pathogens. Wilt caused by Fusarium oxysporum f. sp. ricini is the most complex and destructive disease. Chemical control of wilt has proven ineffective due to the systemic nature of the disease and the seed and soil borne nature of the pathogen. Physical, chemical, biological, and integrated management methods have shown only limited success in controlling wilt. Host plant resistance, however, stands out as the most promising strategy, offering a viable pathway for the genetic improvement of castor beans for wilt resistance. Screening techniques are well established, and several resistant donors have already been identified. While significant progress has been made in understanding the inheritance of wilt resistance, a complete understanding of its genetic mechanisms still requires further research. Single Nucleotide Polymorphism (SNP) markers and genomic regions linked to wilt resistance have been successfully identified. Reniform nematode (Rotylenchulus reniformis) has been identified as a predisposing factor for wilt, and genomic regions linked to nematode resistance have also been identified. However, Marker-assisted selection has not yet been utilized to develop wilt-resistant castor varieties. Further research is required to explore the pathogen diversity, host-pathogen interactions, and mechanisms underlying wilt resistance including the metabolites responsible for preventing the emergence of new pathogenic races and ensuring long-term protection against wilt.

CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson's Disease Associated Protein.

The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinson's disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Of the 1955 molecules predicted by participants in Round 1 of the challenge, 73 were found to bind to LRRK2 in an SPR assay with a KD lower than 150 μM. These 73 molecules were advanced to the Round 2 hit expansion phase, where computational teams each selected up to 50 analogs. Binding was observed in two orthogonal assays for seven chemically diverse series, with affinities ranging from 18 to 140 μM. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants adopted strikingly diverging screening strategies. Machine learning-accelerated methods achieved similar results to brute force (e.g., exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

Red Cell Distribution Width and the Early Detection of Ovarian Cancer

Ovarian Cancer is a leading cause of mortality and morbidity, lacking clinical manifestations and effective early diagnostic techniques. The majority of cases are diagnosed at later stages; however, asymptomatic screening is currently not recommended due to its low predictive value and invasive nature. Furthermore, the current CA-125 biomarker used to monitor Ovarian Cancer is elevated in other conditions, including endometriosis or pelvic inflammation. Red Cell Distribution Width (RDW) is a parameter measured in a complete blood count, measuring the range of variation in red blood cell size and heterogeneity of red blood cell volume. RDW levels positively correlate with Ovarian Cancer progression, additionally increased in Ovarian Cancer groups compared to benign ovarian tumour groups. RDW possesses high sensitivity in distinguishing between cancerous and benign ovarian tumours, in line with ideal screening method standards. RDW additionally shows potential as a diagnostic biomarker in combination with other blood parameters, such as hemoglobin. The hemoglobin-RDW ratio (HRR) acts as an independent predictor of Ovarian Cancer stage. RDW’s mechanism of action is not fully elucidated but can be associated with iron/folic acid deficiency, ineffective hematopoiesis caused by chronic inflammation, or inflammatory cytokines such as IL-1 and IL-17. High RDW levels additionally possess prognostic value, acting as an indicator of low cumulative overall survival and poor chemotherapy responses in various cancers. Further research is needed on RDW’s mechanism of action and its implication as a marker in female cancers, particularly Ovarian Cancer, to implement effective early screening techniques and reduce fatalities.