Conventional Screening Methods Research Articles

Transforming cancer screening: the potential of multi-cancer early detection (MCED) technologies.

Early cancer detection substantially improves the rate of patient survival; however, conventional screening methods are directed at single anatomical sites and focus primarily on a limited number of cancers, such as gastric, colorectal, lung, breast, and cervical cancer. Additionally, several cancers are inadequately screened, hindering early detection of 45.5% cases. In contrast, Multi-Cancer Early Detection (MCED) assays offer simultaneous screening of multiple cancers from a single liquid biopsy and identify molecular changes before symptom onset. These tests assess DNA mutations, abnormal DNA methylation patterns, fragmented DNA, and other tumor-derived biomarkers, indicating the presence of cancer and predicting its origin. Moreover, MCED assays concurrently detect multiple cancers without recommended screening protocols, potentially revolutionizing cancer screening and management. Large trials have reported promising results, achieving 50-95% sensitivity and 89-99% specificity for multiple cancer types. However, challenges, regarding improving accuracy, addressing ethical issues (e.g., psychosocial impact assessment), and integrating MCED into healthcare systems, must be addressed to achieve widespread adoption. Furthermore, prospective multi-institutional studies are crucial for demonstrating the clinical benefits in diverse populations. This review provides an overview of the principles, development status, and clinical significance of MCED tests, and discusses their potential and challenges.

Bioorthogonal Synthesis of Biomimetic Nanoparticles for Screening Chemical Hazards in Food Samples.

The ability to identify unknown risks is the key to improving the level of food safety. However, the conventional nontargeted screening methods for new contaminant identification and risk assessment remain difficult work. Herein, a toxic-oriented screening platform based on high-expression epidermal growth factor receptor HEK293 cell membrane-coated magnetic nanoparticles (EGFR/MNPs) was first used for the discovery of unknown contaminants from food samples. The EGFR served as a bait to bind the risk compounds, considering that the abnormal activation of EGFR was related to the incidence of numerous chronic inflammatory diseases. The cell membranes were specifically covalently immobilized on magnetic carriers through metabolic glycoengineering and strain-promoted alkyne-azide cycloaddition (SPAAC) to ensure the stability and bioactivity of the EGFR/MNPs. The synthesized EGFR/MNPs possessed an excellent receptor loading amount and were used to screen potential risk substances from thermally processed food. Finally, two compounds, harman and norharman, were rapidly identified. The toxicological experiments confirmed that the screened compounds could promote EGFR phosphorylation and further activate downstream signaling pathways, thereby inducing cellular oxidative damage. The proposed method provided a toxic-oriented method for identifying risk compounds from food samples.

Laboratory Methodology for Screening of Cruciferous Host for Egg Laying Preference of Diamondback Moth, Plutella xylostella (L.)

Background: Diamondback moth (DBM), Plutella xylostella (L.), significantly damages Cole crops worldwide, causing major yield losses and management costs. Efficient control measures, including host plant resistance are imperative. While molecular methods offer insights into resistance mechanisms, conventional screening methods remain advantageous. Here, we present a novel laboratory-based oviposition preference screening method for DBM, significantly shortening screening time in resistance breeding. Methods: We studied DBM’s oviposition preferences among cruciferous hosts and plant tissues. Extract was prepared from leaves and heads of dark green and light green cabbage varieties, as well as cauliflower. Aluminum egg-laying sheets were treated with these extracts and introduced into mating boxes with insects. Egg-laying was monitored over seven days, with the number of eggs recorded daily. Result: DBM adults preferred light green cabbage for egg laying (1238±10.54 eggs), with no significant difference between leaf and head tissues. Dark green cabbage was the second choice (514.5±15.78 eggs), while cauliflower was the least preferred (217±37.2 eggs). Interestingly, DBM exhibited varying preferences between leaf and head tissues, with cauliflower heads being favored over leaves. Additionally, we observed distinct day-wise egg laying trends, providing insights into DBM’s oviposition behavior over time. This study demonstrates the efficiency of lab-based screening in identifying resistant crop varieties, aiding sustainable pest management and crop production and highlights the importance of considering host plant selection and tissue specificity in screening of genotype for resistance breeding programs.

High-Throughput Absorbance-Activated Droplet Sorting for Engineering Aldehyde Dehydrogenases.

Recent decades have seen a dramatic increase in the commercial use of biocatalysts, transitioning from energy-intensive traditional chemistries to more sustainable methods. Current enzyme engineering techniques, such as directed evolution, require the generation and testing of large mutant libraries to identify optimized variants. Unfortunately, conventional screening methods are unable to screen such large libraries in a robust and timely manner. Droplet-based microfluidic systems have emerged as a powerful high-throughput tool for library screening at kilohertz rates. Unfortunately, almost all reported systems are based on fluorescence detection, restricting their use to a limited number of enzyme types that naturally convert fluorogenic substrates or require the use of surrogate substrates. To expand the range of enzymes amenable to evolution using droplet-based microfluidic systems, we present an absorbance-activated droplet sorter that allows droplet sorting at kilohertz rates without the need for optical monitoring of the microfluidic system. To demonstrate the utility of the sorter, we rapidly screen a 105-member aldehyde dehydrogenase library towards D-glyceraldehyde using a NADH mediated coupled assay that generates WST-1 formazan as the colorimetric product. We successfully identify a variant with a 51 % improvement in catalytic efficiency and a significant increase in overall activity across a broad substrate spectrum.

High‐Throughput Absorbance‐Activated Droplet Sorting for Engineering Aldehyde Dehydrogenases

AbstractRecent decades have seen a dramatic increase in the commercial use of biocatalysts, transitioning from energy‐intensive traditional chemistries to more sustainable methods. Current enzyme engineering techniques, such as directed evolution, require the generation and testing of large mutant libraries to identify optimized variants. Unfortunately, conventional screening methods are unable to screen such large libraries in a robust and timely manner. Droplet‐based microfluidic systems have emerged as a powerful high‐throughput tool for library screening at kilohertz rates. Unfortunately, almost all reported systems are based on fluorescence detection, restricting their use to a limited number of enzyme types that naturally convert fluorogenic substrates or require the use of surrogate substrates. To expand the range of enzymes amenable to evolution using droplet‐based microfluidic systems, we present an absorbance‐activated droplet sorter that allows droplet sorting at kilohertz rates without the need for optical monitoring of the microfluidic system. To demonstrate the utility of the sorter, we rapidly screen a 105‐member aldehyde dehydrogenase library towards D‐glyceraldehyde using a NADH mediated coupled assay that generates WST‐1 formazan as the colorimetric product. We successfully identify a variant with a 51 % improvement in catalytic efficiency and a significant increase in overall activity across a broad substrate spectrum.

Bibliometric Analysis on of the Impact of Screening to Minimize Maternal Mental Health on Neonatal Outcomes: A Systematic Review.

(1) Background: Prenatal depression, maternal anxiety, puerperal psychosis, and suicidal thoughts affect child welfare and development and maternal health and mortality. Women in low-income countries suffer maternal mental health issues in 25% of cases during pregnancy and 20% of cases thereafter. However, MMH screening, diagnosis, and reporting are lacking. The primary goals of the present study are twofold, as follows: firstly, to evaluate the importance of screening maternal mental health to alleviate perinatal depression and maternal anxiety, and, secondly, to analyze research patterns and propose novel approaches and procedures to bridge the current research gap and aid practitioners in enhancing the quality of care offered to women exhibiting symptoms of perinatal depression. (2) Methods: We conducted a bibliometric analysis to analyze the research topic, using the bibliometric tools Biblioshiny and VOSviewer, as well as the R statistical programming language. To accomplish our goal, we obtained a total of 243 documents from the Scopus and PubMed databases and conducted an analysis utilizing network, co-occurrence, and multiple correlation approaches. (3) Results: Most of the publications in the field were published between the years 2021 and 2024. The results of this study highlight the significance of shifting from conventional screening methods to digital ones for healthcare professionals to effectively manage the symptoms of maternal mental health associated with postpartum depression. Furthermore, the results of the present study suggest that digital screening can prevent maternal physical morbidity, contribute to psychosocial functioning, and enhance infant physical and cognitive health. (4) Conclusions: The research indicates that it is crucial to adopt and include a computerized screening practice to efficiently and immediately detect and clarify the signs of prenatal to neonatal depression. The introduction of digital screening has led to a decrease in scoring errors, an improvement in screening effectiveness, a decrease in administration times, the creation of clinical and patient reports, and the initiation of referrals for anxiety and depression therapy.

PerturBase: a comprehensive database for single-cell perturbation data analysis and visualization.

Single-cell perturbation (scPerturbation) sequencing techniques, represented by single-cell genetic perturbation (e.g. Perturb-seq) and single-cell chemical perturbation (e.g. sci-Plex), result from the integration of single-cell toolkits with conventional bulk screening methods. These innovative sequencing techniques empower researchers to dissect perturbation effects in biological systems at an unprecedented resolution. Despite these advancements, a notable gap exists in the availability of a dedicated database for exploring scPerturbation data. To address this gap, we present PerturBase, the most comprehensive database designed for the analysis and visualization of scPerturbation data (http://www.perturbase.cn/). PerturBase curates 122 datasets from 46 publicly available studies, covering 115 single-modal and 7 multi-modal datasets that include 24254 genetic and 230 chemical perturbations from approximately 5 million cells. The database, comprising the 'Dataset' and 'Perturbation' modules, provides insights into various results, encompassing quality control, denoising, differential gene expression analysis, functional analysis of perturbation effectsand characterization of relationships between perturbations. All the datasets and results are presented on user-friendly, easy-to-browse web pages and can be visualized through intuitive and interactive plot and table formats. In summary, PerturBase stands as a pioneering, high-content database intended for searching, visualizingand analyzing scPerturbation datasets, contributing to a deeper understanding of perturbation effects.

Pre-clinical study of IR808 dye for cervical cancer in vitro and in vivo imaging.

There is an urgent need for improved methods for early screening and rapid diagnosis of cervical cancer since current conventional screening methods are plagued by operator subjectivity and unnecessary biopsies. IR808 is a tumour-targeting near-infrared (NIR) fluorescent dye that permits NIR imaging without the requirement of chemical conjugation. Our study investigates an IR808-based strategy for real-time monitoring of the cervix in vivo and rapid assessment of cervical specimens in vitro. We investigated the uptake of IR808 in vitro using normal cervical epithelial cells and three cervical cancer cell lines. The biodistribution of IR808 was examined in vivo via intravenous injection into tumour-bearing mice. Additionally, in vitro tissues were stained with IR808 to simulate the identification of cervical tumors in the clinical setting. Biocompatibility of the dye in both cellular and animal models was also examined. IR808 exhibited significant tumour-to-background ratios in fluorescence molecular imaging of in vivo tumors in nude mice. The application of NIR fluorescent dye IR808 in specific imaging screening, safe and non-invasive real-time monitoring, and rapid identification of cervical tumors from tissue specimens is expected to improve current screening methods for cervical cancer.

Temporal dynamics in psychological assessments: a novel dataset with scales and response times

The growing prevalence of mental health issues underscores the need for innovative screening methods. Large-scale, internet-based psychological screening has emerged as a vital tool to accurately determine morbidity rates and facilitate early diagnosis of mental disorders. However, conventional psychological screening methods often struggle with non-genuine responses and lack objective metrics. To bridge this gap, we have compiled a novel dataset derived from an expansive screening initiative at Xinxiang Medical University. The study, conducted from February 27 to March 17, 2021, yielded a dataset comprising responses from 24,292 students to four well-established psychological scales—PHQ-9, GAD-7, ISI, and PSS. A distinctive feature of this dataset is the inclusion of response time data, which captures the temporal dynamics of participants’ interactions with the scales, offering valuable insights into their response behaviour. The release of this dataset offers a substantial opportunity for researchers in the domains of psychology and public health to explore new insights into mental health, scale reliability, and the dynamics of psychological assessment.

A Practical In Silico Method for Predicting Compound Brain Concentration-Time Profiles: Combination of PK Modeling and Machine Learning.

Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the measurement of (unbound fraction of drug) brain-to-plasma partition coefficients, it is difficult to consider nonequilibrium between plasma and brain compound concentration-time profiles. To truly understand the pharmacokinetics/pharmacodynamics of CNS drugs, compound concentration-time profiles in the brain are necessary; however, such analyses are costly and time-consuming and require a significant number of animals. Therefore, in this study, we attempted to develop an in silico prediction method that does not require a large amount of experimental data by combining modeling and simulation (M&S) with machine learning (ML). First, we constructed a hybrid model linking plasma concentration-time profile to the brain compartment that takes into account the transit time and brain distribution of each compound. Using mouse plasma and brain time experimental values for 103 compounds, we determined the brain kinetic parameters of the hybrid model for each compound; this case was defined as scenario I (a positive control experiment) and included the full brain concentration-time profile data. Next, we built an ML model using chemical structure descriptors as explanatory variables and rate parameters as the target variable, and we then input the predicted values from 5-fold cross-validation (CV) into the hybrid model; this case was defined as scenario II, in which no brain compound concentration-time profile data exist. Finally, for scenario III, assuming that the brain concentration is obtained at only one time point, we used the brain kinetic parameters from the result of the 5-fold CV in scenario II as the initial values for the hybrid model and performed parameter refitting against the observed brain concentration at that time point. As a result, the RMSE/R2-values of the brain compound concentration-time profiles over time were 0.445/0.517 in scenario II and 0.246/0.805 in scenario III, indicating the method provides high accuracy and suggesting that it is a practical method for predicting brain compound concentration-time profiles.

Clear zone formation in microdroplets for high-throughput screening for lactic acid bacteria.

Droplet microfluidic-based technology is a powerful tool for biotechnology, and it is also expected that it will be applied to culturing and screening methods. Using this technology, a new high-throughput screening method for lactic acid bacteria was developed. In this study, the conventional culture of lactic acid bacteria that form clear zones on an agar medium was reproduced in water-in-oil droplets, and only the droplets in which lactic acid bacteria grew were collected one by one. Using this method, the specific recovery of Lactiplantibacillus plantarum from a mixture of L. plantarum and Escherichia coli and the acquirement of lactic acid bacteria from an environmental sample were successful. This method could be applied to various conventional screening methods using the clear zone as a microbial growth indicator. This has expanded the possibilities of applying droplet microfluidic-based technology to microbial cultivations.

Insight of recent artificial intelligence-based strategy to effectively screen COVID-19

<p><span lang="EN-US">The recent era of pandemic by corona virus disease (COVID-19) has witnessed a faster evolution of various technological solution to thwart the life-threating situation. The most important step was to select a faster mode of screening COVID-19 using chest x-ray (CXR) which could be actually ten folds faster than conventional invasive screening methods. However, the method of determining the presence of COVID-19 from CXR is critically challenging owing to the dynamic and complex nature of disease. Such problem is attempted to be solved by harnessing the potential of artificial intelligence (AI). Hence, this paper contributes towards discussion of most recent and current implementation strategies formulated by AI models towards diagnosing COVID-19. The study outcome of this paper yields an interesting learning outcome to show that AI models’ adoption is increasing in faster pace and yet challenges do exist till date. The outcome of study will assist in better adoption of AI models towards screening COVID-19.</span></p>

Performance of a Large Language Model in Screening Citations

Large language models (LLMs) are promising as tools for citation screening in systematic reviews. However, their applicability has not yet been determined. To evaluate the accuracy and efficiency of an LLM in title and abstract literature screening. This prospective diagnostic study used the data from the title and abstract screening process for 5 clinical questions (CQs) in the development of the Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock. The LLM decided to include or exclude citations based on the inclusion and exclusion criteria in terms of patient, population, problem; intervention; comparison; and study design of the selected CQ and was compared with the conventional method for title and abstract screening. This study was conducted from January 7 to 15, 2024. LLM (GPT-4 Turbo)-assisted citation screening or the conventional method. The sensitivity and specificity of the LLM-assisted screening process was calculated, and the full-text screening result using the conventional method was set as the reference standard in the primary analysis. Pooled sensitivity and specificity were also estimated, and screening times of the 2 methods were compared. In the conventional citation screening process, 8 of 5634 publications in CQ 1, 4 of 3418 in CQ 2, 4 of 1038 in CQ 3, 17 of 4326 in CQ 4, and 8 of 2253 in CQ 5 were selected. In the primary analysis of 5 CQs, LLM-assisted citation screening demonstrated an integrated sensitivity of 0.75 (95% CI, 0.43 to 0.92) and specificity of 0.99 (95% CI, 0.99 to 0.99). Post hoc modifications to the command prompt improved the integrated sensitivity to 0.91 (95% CI, 0.77 to 0.97) without substantially compromising specificity (0.98 [95% CI, 0.96 to 0.99]). Additionally, LLM-assisted screening was associated with reduced time for processing 100 studies (1.3 minutes vs 17.2 minutes for conventional screening methods; mean difference, -15.25 minutes [95% CI, -17.70 to -12.79 minutes]). In this prospective diagnostic study investigating the performance of LLM-assisted citation screening, the model demonstrated acceptable sensitivity and reasonably high specificity with reduced processing time. This novel method could potentially enhance efficiency and reduce workload in systematic reviews.

Discovering virtual antiperovskites as solid-state electrolytes through active learning

In surveying an extensive library of 18,133 hypothetical antiperovskites (X3BA), we address the challenges posed by conventional experimental and computational screening methods. We introduce a novel computational approach, leveraging an active learning framework that synergistically integrates genetic algorithms with Bayesian optimization. This method efficiently discerns thermodynamically stable antiperovskites, narrowing down the vast initial set to 43 compounds characterized by minimal energy above the hull (Eh<150 meV/atom). Subsequent evaluations of their band structure (Eg), electrochemical stability window (Vw), and room-temperature lithium ion conductivity (σRT), alongside the construction of a 4-dimensional Pareto frontier for Eh, Eg, Vw, and σRT, refined this list to 22 promising candidates, seven of which exhibit outstanding room-temperature ionic conductivity (>4 mS cm−1), marking them as potential candidates. Our methodology not only expedites the identification of superior antiperovskites but also establishes a groundbreaking paradigm for computational exploration in materials science.

Consensus holistic virtual screening for drug discovery: a novel machine learning model approach

In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula “w_new”, consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology.Scientific contributionWe presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced ‘w_new’, a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains.Graphical

Utilizing Constructed Neural Networks for Autism Screening

Autism Spectrum Disorder is known to cause difficulties in social interaction and communication, as well as repetitive patterns of behavior, interests, or hobbies. These challenges can significantly affect the individual’s daily life. Therefore, it is crucial to identify and assess children with Autism Spectrum Disorder early to significantly benefit the long-term health of children. Unfortunately, many children are not diagnosed or are misdiagnosed, which means they miss out on the necessary interventions. Clinicians and other experts face various challenges during the diagnostic process. Digital tools can facilitate early diagnosis effectively. This study aimed to explore the use of machine learning techniques on a dataset collected from a serious game designed for children with autism to investigate how these techniques can assist in classification and make the clinical process more efficient. The responses were gathered from children who participated in interactive games deployed on mobile devices, and the data were analyzed using various types of neural networks, such as multilayer perceptrons and constructed neural networks. The performance metrics of these models, including error rate, precision, and recall, were reported, and the comparative experiments revealed that the constructed neural network using the integer rule-based neural networks approach was superior. Based on the evaluation metrics, this method showed the lowest error rate of 11.77%, a high accuracy of 0.75, and a good recall of 0.66. Thus, it can be an effective way to classify both typically developed children and children with Autism Spectrum Disorder. Additionally, it can be used for automatic screening procedures in an intelligent system. The results indicate that clinicians could use these techniques to enhance conventional screening methods and contribute to providing better care for individuals with autism.

Development and validation of a new colour vision screener: A multi‐centre study

Aims/Purpose: The detection of acquired and congenital colour vision deficiency is of importance in the clinic for the early detection of ocular disease and determination of occupational suitability. Conventional methods of colour vision screening often fail to pass all normals and/or correctly identify those with acquired and congenital colour vision deficiency. The Colour Vision Screener (CVS) test [1] was developed to utilize an improved understanding of colour vision mechanisms to overcome the limitations present in conventional colour screening methods. This study aimed to use a robust multi‐centre methodology to fully validate the CVS test.Methods: Over 400 participants, including 124 normal trichromats, 166 deutans and 62 protans, were recruited from 10 international Colour Assessment and Diagnosis (CAD) testing centres. Each participant completed the CVS and the CAD test with calibrated visual displays. The red/green (RG) and yellow/blue (YB) CVS test sensitivity and specificity were established. The RG statistics were compared to the ‘gold standard’ reference for screening for RG congenital colour vision deficiency—the Ishihara pseudoisochromatic plate test.Results: The CVS test correctly identified ~99.1% of normal trichromats. All subjects with congenital RG loss and acquired loss on top of congenital RG deficiency were identified as having abnormal colour vision by the CVS. The Ishihara test, based upon 0 errors on the first 25 plates of the 38‐plate edition, correctly identifies ~80.6% of normal trichromats and 99.5% of subjects with congenital RG loss.Conclusions: Data collected across 10 international CAD centres demonstrate that the CVS can achieve a high sensitivity without sacrificing specificity. This multi‐centre study demonstrates that the CVS is quick, efficient, and easy to use and allows for the rapid and reliable screening of congenital and acquired colour vision deficiencies in large populations.Reference1. Barbur JL, Rodriguez‐Carmona M, Evans BEW. Colour vision assessment‐3. An efficient, two‐step, colour assessment protocol. Colour Research &amp; Application 2021; 46: 33–45. https://doi.org/10.1002/col.22599

Bioelectrical impedance parameters add incremental value to waist-to-hip ratio for prediction of metabolic dysfunction associated steatotic liver disease in youth with overweight and obesity.

Metabolic dysfunction-associated steatotic liver disease (MASLD) presents a growing health concern in pediatric populations due to its association with obesity and metabolic syndrome. Bioelectrical impedance analysis (BIA) offers a non-invasive and potentially effective alternative for identifying MASLD risk in youth with overweight or obesity. Therefore, this study aimed to assess the utility of BIA for screening for MASLD in the youth. This retrospective, cross-sectional study included 206 children and adolescents aged <20 years who were overweight and obese. The correlations between anthropometric measurements and BIA parameters and alanine aminotransferase (ALT) levels were assessed using Pearson's correlation analysis. Logistic regression analysis was performed to examine the associations between these parameters and ALT level elevation and MASLD score. Receiver operating characteristic (ROC) curves were generated to assess the predictive ability of the parameters for MASLD. Pearson's correlation analysis revealed that waist-to-hip ratio (WHR), percentage body fat (PBF), and BIA parameters combined with anthropometric measurements were correlated with ALT level. Logistic regression revealed that WHR, skeletal muscle mass/WHR, PBF-WHR, fat-free mass/WHR, and appendicular skeletal muscle mass/WHR were correlated with ALT level elevation after adjusting for age, sex, and puberty. WHR, PBF-WHR, and visceral fat area (VFA)-WHR were positively correlated with the MASLD score in the total population after adjusting for age, sex, and puberty. PBF-WHR and VFA-WHR were correlated with the MASLD score even in youth with a normal ALT level. The cutoff points and area under the ROC curves were 34.6 and 0.69 for PBF-WHR, respectively, and 86.6 and 0.79 for VFA-WHR, respectively. This study highlights the utility of combining BIA parameters and WHR in identifying the risk of MASLD in overweight and obese youth, even in those with a normal ALT level. BIA-based screening offers a less burdensome and more efficient alternative to conventional MASLD screening methods, facilitating early detection and intervention in youth at risk of MASLD.

Evaluating Clinical Efficacy of Thermalytix as a Breast Cancer Screening Tool in Young Women

Abstract Objective: Breast cancer remains a significant health challenge worldwide, particularly among women under 45 years of age who often present with dense breast tissue. Mammography, the current gold standard for breast cancer screening, is less effective in this demographic due to reduced sensitivity in detecting malignancies within dense breast tissue. This study evaluates the efficacy of Thermalytix, an artificial intelligence-driven, noninvasive, and radiation-free thermal imaging tool, as a screening modality for breast cancer in young women through a meta-analysis of published studies. Materials and Methods: This meta-analysis aggregated the data from three clinical studies involving a total of 1187 women who first underwent a Thermalytix test followed by reference standard of care (SoC) tests, which included one or more mammography, ultrasound, and biopsy. Among these women, 463 were under 45 years of age and were eligible for this study. Thermalytix analyzed high-resolution thermal images of the breast, utilizing novel radiomic features such as hotspots, vascular patterns, and areolar characteristics to predict malignancy. The performance of Thermalytix was evaluated by computing its sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), with 95% confidence intervals (95% CIs), in both the overall population and the younger cohort by aggregating the data from all studies. Results: Among the 463 young women under 45 years, 43 were diagnosed with breast cancer as per SoC. When raw data were aggregated from this young women cohort, Thermalytix resulted in a sensitivity of 90.7% (95% CI: 82.0%–99.4%), a specificity of 82.1% (95% CI: 78.5%–85.8%), a PPV of 34.2% (95% CI: 25.5%–42.9%), and an NPV of 98.9% (95% CI: 97.7%–100%). The pooled sensitivity and specificity of Thermalytix using the random-effects model were estimated to be 96.0% (95% CI: 88.9%–100%) and 82.3% (95% CI: 78.6%–85.9%), respectively. Further, in the entire population of 1187, Thermalytix showed an aggregated sensitivity of 88.3% (95% CI: 83.3%–93.2%), specificity of 84.7% (95% CI: 82.5%–86.9%), PPV of 47.7% (95% CI: 42.0%–53.3%), and NPV 97.9% (95% CI: 96.9%–98.8%). Conclusions: Thermalytix demonstrated high sensitivity and NPV in women under 45 years of age, suggesting its potential as an effective screening modality for younger women who face challenges with conventional screening methods due to dense breast tissue.

High-throughput screening technologies for drug discovery

The development of high-throughput screening technologies has revolutionized the field of drug discovery by significantly improving the efficiency of compound library screening. Traditional screening methods, such as manual screening and biochemical assays, were time-consuming and limited in their ability to identify lead compounds. However, the advent of high-throughput screening technologies has overcome these limitations and provided researchers with a more efficient and effective approach. This review begins by examining the background, characteristics, and limitations of conventional screening methods. These methods often required large amounts of time, resources, and labor, making them impractical for large-scale compound screening. In recent years, some new technologies have emerged, including virtual screening, image analysis, prediction methods, and microarray-based screening. Each of these approaches has its own strengths and limitations, but collectively they have greatly enhanced the efficiency and accuracy of compound identification. These viewpoints highlight the successful application of these technologies in identifying lead compounds for various therapeutic targets. Finally, the review envisions the future development of high-throughput screening technologies. It emphasizes the need for continuous optimization and innovation to further improve the efficiency and effectiveness of compound identification. The ultimate goal is to shorten drug development timelines and provide high-quality lead compounds for the benefit of patients. In conclusion, the emergence of high-throughput screening technologies has significantly improved the efficiency of compound library screening and provided better lead compounds for drug discovery. Ongoing advancements in these technologies hold great promise for the future of pharmaceutical research and development.