Early-stage Detection Research Articles

The use of micronuclei DNA from erythrocytes for the detection of early-stage gastrointestinal cancers.

4085 Background: Gastrointestinal (GI) cancers account for over a third of cancer deaths. However, their early detection remains a challenge. Micronuclei (MN) are extranuclear bodies containing damaged chromosome segments indicative of genomic instability. Increased MN frequency in hematopoietic cells is observed in patients with solid tumors. We have developed a method (WO2021/228246 A1) for purifying and characterizing micronuclei DNA (MN-DNA) in erythrocytes from peripheral blood. By comparing MN-DNA from healthy donors (HDs) and GI cancer patients, we identified significant changes in read densities at specific genomic locations in patients, which were termed as tumor-associated MN-DNA (taMN-DNA) signatures. Here, we evaluated the potential of these signatures for early-stage GI cancer detection, including colorectal (CC), gastric (GC) and esophageal (EC) cancers. Methods: Peripheral blood (1-2 ml) was collected from healthy donors (HD) and cancer patients MN-DNA isolation and purification from erythrocytes. Participants were randomly divided into training, validation and independent test cohorts in a 7:2:1 ratio, maintaining similar cancer types, CC/GC/EC/HD ratio, gender and age distribution. Distinct MN-DNA features between cancer patients and HDs were identified using sequencing data. Logistic regression algorithms were applied using these features for cancer detection. Results: The study enrolled 1753 participants, including 987 HDs, 420 CC, 282 GC and 64 EC cases. Of these, over half were diagnosed with early-stage diseases; TNM stage 0-I accounted for 40.57%, stage II for 23.32%, stage III for 29.25% and stage IV for 6.87%. The cancer detection model with MN-DNA features yielded an overall accuracy of 87.43%, with a sensitivity of 83.66% and specificity of 90.36%. For individual caner types, sensitivity was 85.37% for CC, 79.31% for GC and 92.31% for EC. For early-stage (stage 0-II) CC, GC and EC, the model demonstrated sensitivities of 95.12%, 86.05% and 100.00%, respectively, at 90.36% specificity. Conclusions: Unlike cell-free DNA, MN-DNA are chromosomal fragments in the cytoplasm. The abundance of erythrocytes in peripheral blood provides an easily accessible source for MN-DNA enrichment. This pilot study illustrates the potential of MN-DNA as a tool for early GI cancer detection, contributing to large-scale efforts towards developing an effective GI cancer screening test.

FCM-DCS: Fuzzy C means distorted contour-based segmentation model for breast cancer detection

Breast cancer (BC) is a commonly diagnosed cancer among women nowadays. The cancer cells in the breast tissues are known as BC. Comprehensive research on early-stage BC detection helped to increase the survival rate and reduce the amortality rate associated with this disease. Mammogram scan analysis is a commonly used breast tissue visualization method. This image data is analyzed adequately for accurate BC diagnosis. Region of interest (ROI) identification is crucial in an image-based BC detection system. The ROI detection helps to segment the cancer tissues from the mammogram images by analyzing the heterogeneity among cancerous and normal breast tissues. Early-stage BC issues have homogeneous features as normal breast tissues. So, it is an open challenge for the researchers to develop a more accurate segmentation method during the automatic BC stages detection system. This study introduced fuzzy C means (FCM) distorted contour-based segmentation (FCM DCS) method to address the real detection issues in present studies. It uses the distorted contour (DC) based method to identify the contour of the cancer tissue from mammogram images. The DC method is performed with the help of FCM to identify the cancer tissues. Moreover, a histogram and adaptive equalization method were utilized to reduce image noise and preserve the edge features. The result analysis shows that the FCM-DC methods achieved a maximum accuracy rate (98.76 %) than comparison methods in BC detection.

Assessment of plasma cell-free DNA fragmentation for multi-cancer early detection: An independent clinical validation study.

e15037 Background: Cell-free DNA (cfDNA) in the circulation has gained significant attention due to its potential applications for non-invasive early cancer detection. Fragmentomics of cfDNA is emerging as a promising field of biomarker research, exhibiting the capability to sensitively detect multiple cancer types. In previous retrospective research, we developed an approach called PatternWGS for comprehensive analysis of cfDNA fragmentation patterns and presented a multi-cancer detection model. In this study, we aim to evaluate the performance of PatternWGS in an independent validation cohort including lung, breast, colorectal, liver, and gastric cancers from a new clinical center. Methods: Participants with confirmed diagnosis of lung, breast, colorectal or liver cancer were enrolled in the cancer group. Non-cancer participants without known presence of malignancies were recruited from the same clinical center. Plasma samples from patients with lung (n=47), breast (n=19), colorectal (n=14), liver (n=8) and gastric (n=6) cancers, along with 101 non-cancer individuals were collected in this study. cfDNA was isolated from plasma and stored within a month prior to the whole genome sequencing. The characteristics of cfDNA fragmentation integrating fragment size profiles, motif patterns, genomic coverage distributions were utilized to evaluate the performance for cancer signal detection. In addition, the performance of estimated cfDNA tumor fraction, using copy number aberrations (ichorCNA), was compared with our approach. Results: 94 patients with one of five types of cancers and 101 non-cancer controls were prospectively enrolled between July 2023 and November 2023. Among the cancer group, 64% were at early stages (Stage I: 48%, Stage II: 16%). A significantly higher proportion of shorter fragments was observed in cancer group, and the degree increased with increasing cancer stage. Overall sensitivity across cancer types and stages was 72.3%, and the specificity for non-cancer controls was 80%. As expected, the sensitivity of cancer detection is higher in late-stage patients compared to those in the early-stages (61% in stage I, 80% in stage II, 84.6% in stage III, and 91.7% in stage IV). The sensitivity of detecting lung (72% had stage I cancer), breast, colorectal, liver, and gastric cancers reached 57.4%, 84.2%, 85.7%, 87.5% and 100% respectively. The detection performance using cfDNA tumor fraction by ichorCNA demonstrated a relatively low overall sensitivity of 28.7% (24.6% for stage I-II, 44% for stage III-IV). Conclusions: This independent clinical validation study, which focused on exploring plasma cfDNA fragmentation, demonstrated ideal performance in early-stage cancer detection. The cfDNA fragmentation-based approach exhibited a superior sensitivity in detecting early cancer signals compared with copy number aberrations-based approach.

SE-GAP-ResNet: A machine learning model for abnormal breast detection.

e12579 Background: Breast cancer is the most common cancer in women worldwide. Although screening has improved early-stage detection, challenges like high false positives and heavy workloads for radiologists persist, emphasizing the importance of early identification of breast irregularities, including masses and micro-calcifications. Methods: We use a subset of the mini-MIAS dataset, randomly choosing 100 abnormal and 100 healthy images, with abnormal images categorized into six sub-types. Preprocessing involves removing noises and excluding background and muscle regions. Our classifier is the ResNet-34 model, a deep convolutional neural network with 34 layers, known for effective pattern identification in medical images. Enhancements include: (1) The Squeeze-and-Excitation (SE) block boosts neural network discriminative power by dynamically recalibrating channel or feature importance. Integrated into ResNet-34, it forms the backbone network, enhancing the identification of relevant patterns for improved diagnostic accuracy. (2) Complex patterns at the system's output are condensed into a common disease pattern through global average pooling (GAP). This reduces model parameters, enhances interpretability, and facilitates the extraction of essential diagnostic information from the entire image. (3) Feature vectors from different layers are concatenated to merge low-level and high-level features. Low-level features encompass fundamental visual attributes such as pixel intensity and texture, while high-level features involve more complex patterns and structures, including organ shapes and anomalies. (4) We employ multiple-way data augmentation techniques to augment the training set, including rotation, translation, scaling, Gamma correction, and shear. The resultant model is referred to as SE-GAP-ResNet. Results: After 10 iterations of 10-fold cross-validation, SE-GAP-ResNet exhibited notable performance with a sensitivity of 95.60%, specificity of 93.30%, precision of 93.49%, accuracy of 94.45%, and an F1 score of 94.51%. Outperforming three state-of-the-art approaches, as shown in the table, two-sample t-tests confirm that SE-GAP-ResNet achieves significantly superior performance (p<0.05). Conclusions: SE-GAP-ResNet excels in identifying breast abnormalities. We aim to enhance the AI system by adding a multi-class diagnosis framework and the capability to predict lesion regions, with the goal of substantially reducing callback rates for abnormal breast diagnoses and improving diagnostic accuracy and efficiency. [Table: see text]

Achieving World Health Organization Global Breast Cancer Initiative targets: A situational analysis and action plan in Nigeria.

e13782 Background: Breast cancer [BC] is the leading cause of cancer incidence and mortality in Nigerian women, with five-year survival of 44% vs. 90% in the US. The World Health Organization [WHO], through the Global Breast Cancer Initiative [GBCI], endorses a 3-pillar program that promotes early-stage detection, timely diagnosis, and expeditious treatment. We performed a situational analysis to understand predictors of achieving WHO targets in Nigeria as a basis for implementing an action plan. Methods: We conducted a cross-sectional study using a prospective multi-institutional BC patient database in Nigeria (funded by ASCO), which was expanded sequentially from a single institution in May 2022 to seven institutions representing all geopolitical zones by May 2023. All newly diagnosed BC patients enrolled in the database from 2022-2024 were included. We analyzed univariate associations with WHO GBCI key performance indicators [KPIs] using logistic regression and global p-values. Results: Five-hundred and seventy-one patients were included. Median age was 48 years (IQR 40,56). Conclusions: The utilization of a multi-institutional BC database inclusive of all geopolitical zones in Nigeria provides vital insight for monitoring progress toward WHO targets. Our findings suggest that breast health clinical encounters (through CBE or mammogram) prior to developing BC are associated with reduced diagnostic delays and early-stage BC diagnosis. For those with BC, multiple touchpoints with the healthcare system are associated with increased delays and late-stage diagnosis, which justifies the need for developing patient navigation programs. [Table: see text]

Calcium plays an essential role in early-stage dendrite injury detection and regeneration

Dendrites are injured in a variety of clinical conditions such as traumatic brain and spinal cord injuries and stroke. How neurons detect injury directly to their dendrites to initiate a pro-regenerative response has not yet been thoroughly investigated. Calcium plays a critical role in the early stages of axonal injury detection and is also indispensable for regeneration of the severed axon. Here, we report cell and neurite type-specific differences in laser injury-induced elevations of intracellular calcium levels. Using a human KCNJ2 transgene, we demonstrate that hyperpolarizing neurons only at the time of injury dampens dendrite regeneration, suggesting that inhibition of injury-induced membrane depolarization (and thus early calcium influx) plays a role in detecting and responding to dendrite injury. In exploring potential downstream calcium-regulated effectors, we identify L-type voltage-gated calcium channels, inositol triphosphate signaling, and protein kinase D activity as drivers of dendrite regeneration. In conclusion, we demonstrate that dendrite injury-induced calcium elevations play a key role in the regenerative response of dendrites and begin to delineate the molecular mechanisms governing dendrite repair.

Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques.

Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, and prone to ambiguous interpretations. This study proposes an advanced machine learning model designed to enhance lung cancer stage classification using CT scan images, aiming to overcome these limitations by offering a faster, non-invasive, and reliable diagnostic tool. Utilizing the IQ-OTHNCCD lung cancer dataset, comprising CT scans from various stages of lung cancer and healthy individuals, we performed extensive preprocessing including resizing, normalization, and Gaussian blurring. A Convolutional Neural Network (CNN) was then trained on this preprocessed data, and class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE). The model's performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and ROC curve analysis. The results demonstrated a classification accuracy of 99.64%, with precision, recall, and F1-score values exceeding 98% across all categories. SMOTE significantly enhanced the model's ability to classify underrepresented classes, contributing to the robustness of the diagnostic tool. These findings underscore the potential of machine learning in transforming lung cancer diagnostics, providing high accuracy in stage classification, which could facilitate early detection and tailored treatment strategies, ultimately improving patient outcomes.

Early-Stage Fault Diagnosis of Motor Bearing Based on Kurtosis Weighting and Fusion of Current-Vibration Signals.

To solve the problem of a low signal-to-noise ratio of fault signals and the difficulty in effectively and accurately identifying the fault state in the early stage of motor bearing fault occurrence, this paper proposes an early fault diagnosis method for bearings based on the Differential Local Mean Decomposition (DLMD) and fusion of current-vibration signals. This method uses DLMD to decompose the current signal and vibration signal, respectively, and weights the decomposed product function (PF) according to the kurtosis value to reconstruct the signal, and then fuses the reconstructed signals to obtain the current-vibration fusion signal after normalization, and then analyzes the fusion signal spectrally through the Hilbert envelope spectrum. Finally, the fusion signal is analyzed by the Hilbert envelope spectrum, and a clear fault characteristic frequency is obtained. The experimental results demonstrate that compared to traditional bearing fault diagnosis methods, the proposed method significantly improves the signal-to-noise ratio of fault signals, effectively enhances the sensitivity of early-stage fault detection in motor bearings, and improves the accuracy of fault identification.

Status of breast cancer detection in young women and potential of liquid biopsy.

Young onset breast cancer (YOBC) is an increasing demographic with unique biology, limited screening, and poor outcomes. Further, women with postpartum breast cancers (PPBCs), cancers occurring up to 10 years after childbirth, have worse outcomes than other young breast cancer patients matched for tumor stage and subtype. Early-stage detection of YOBC is critical for improving outcomes. However, most young women (under 45) do not meet current age guidelines for routine mammographic screening and are thus an underserved population. Other challenges to early detection in this population include reduced performance of standard of care mammography and reduced awareness. Women often face significant barriers in accessing health care during the postpartum period and disadvantaged communities face compounding barriers due to systemic health care inequities. Blood tests and liquid biopsies targeting early detection may provide an attractive option to help address these challenges. Test development in this area includes understanding of the unique biology involved in YOBC and in particular PPBCs that tend to be more aggressive and deadly. In this review, we will present the status of breast cancer screening and detection in young women, provide a summary of some unique biological features of YOBC, and discuss the potential for blood tests and liquid biopsy platforms to address current shortcomings in timely, equitable detection.

Folate Receptor Targeting Mn(II) Complex Encapsulated Porous Silica Nanoparticle as an MRI Contrast Agent for Early-State Detection of Cancer.

Cancer is recognized as one of the major causes of mortality, however, early-stage detection can increase the survival chance greatly. It is recognized that folate receptors are gradually overexpressed in the cellular membrane with the progress of cancer from stage 1 to stage 4. Utilizing the fact, herein, developed a porous silica nanoparticle system C1@SiO2-FA-NP; A) impregnated with thermodynamically stable Mn(II) complex (1) molecules within the core of the nanoparticle, and B) surface functionalized with folate units. It exhibited a high longitudinal relaxivity value r1 = 21.45 mM-1s-1 that substantially increased to r1 = 40.97 mM-1s-1 in the presence of 0.67mM concentration of BSA under the physiological condition. The in vitro fluorescent images after surface conjugation of C1@SiO2-FA-NP with FITC (fluorescein isothiocyanate) buttressed the inclusion of the nanoparticle exclusively within the cancerous HeLa cells than that of healthy HEK293 cells. The importance of the surface-bound folate unit in the nanoparticle is further established by comparing the fluorescent images of HeLa cells in the absence of the group. Finally, the applicability of C1@SiO2-FA-NP as the T1-weighted MRI contrast agent for early-stage cancer diagnosis is established within C57BL/6 mice after infecting the mice with HeLa cells.

An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection.

In clinical decision-making for chronic disorders like chronic kidney disease, high variability often leads to uncertainty and negative outcomes. Deep learning techniques have been developed as useful tools for minimizing the chance and improving clinical decision-making. Moreover, traditional techniques for chronic kidney disease recognition frequently the accuracy is compromised as it relies on limited sets of biological attributes. Therefore, in the proposed work, a combination of deep radial bias network and the puma optimization algorithm is suggested for precised chronic kidney disease classification. Initially, the accessed data undergo preprocessing using Spectral Z score Bag Boost K-Means SMOTE transformation, which includes robust scaling, data cleaning, balancing, encoding, handling missing values, min-max scaling, and z-standardization. Feature selection is then conducted using the hybrid methodology of Role-oriented Binary Walrus Grey Wolf Algorithm to choose discriminative features for improving classification accuracy. Then, Auto Encoder with Patch-Based Principal Component Analysis is employed for dimensionality reduction to minimize the processing time. Finally, the proposed classification method utilizes deep radial bias and the puma optimization search algorithm for effective chronic kidney disease classification. The introduced scheme is tested on two datasets: the risk factor prediction of chronic kidney disease dataset and chronic kidney disease dataset, which provides accuracies of 99.02%, and 99.15%, respectively. Experiments demonstrate that the proposed model identifies chronic kidney disease more accurately than the existing approaches.

A Novel FSS Loaded Circular Slot Monopole Antenna for Microwave Breast Imaging Application

Breast cancer continues to be a major contributor to women's global mortality rates, emphasizing the need to explore alternative and supplementary early-stage detection techniques. This study delves into a circular slot monopole ultra-wideband (UWB) antenna equipped with a frequency-selective surface (CSMA-FSS) for early detection microwave imaging applications. To expand the impedance bandwidth of the traditional circular monopole antenna, a circular slit is introduced on a monopole structure. Additionally, a 10 × 10 unit cell FSS structure is integrated into the antenna as a far-field reflecting plane to enhance gain characteristics. The antenna, crafted with dimensions of 50 × 50 × 1.6 mm³ and a 1.6 mm thick FR4 substrate, operates across the ultra-wideband frequency spectrum, spanning from 3.1 GHz to 12 GHz, while consistently maintaining a reflection coefficient (S 11) value of – 10 dB or lower. Notably, the antenna showcases a gain of over 5 dBi across the entire 3.1 GHz to 12 GHz frequency range. The suggested antenna is ideal for microwave imaging applications due to its broad impedance bandwidth, impressive gain, and excellent directivity. Furthermore, it maintains a relatively uniform group delay, with a deviation of ±0.5 ns throughout the UWB band. In the time domain characteristics, the antenna exhibits excellent pulse handling capabilities and strong signal correlation, featuring an S 21 value of less than – 25 dB. The characteristics, observed in both the time and frequency domains, suggest that the proposed antenna holds significant potential for substantially enhancing breast cancer imaging system.

Differentially expressed genes associated with high metabolic tumor volume served as diagnostic markers and potential therapeutic targets for pancreatic cancer

BackgroundThe lack of distinct biomarkers for pancreatic cancer is a major cause of early-stage detection difficulty. The pancreatic cancer patient group with high metabolic tumor volume (MTV), one of the values measured from positron emission tomography—a confirmatory method and standard care for pancreatic cancer, showed a poorer prognosis than those with low MTV. Therefore, MTV-associated differentially expressed genes (DEGs) may be candidates for distinctive markers for pancreatic cancer. This study aimed to evaluate the possibility of MTV-related DEGs as markers or therapeutic targets for pancreatic cancer.MethodsTumor tissues and their normal counterparts were obtained from patients undergoing preoperative 18F-FDG PET/CT. The tissues were classified into MTV-low and MTV-high groups (7 for each) based on the MTV2.5 value of 4.5 (MTV-low: MTV2.5 < 4.5, MTV-high: MTV2.5 ≥ 4.5). Gene expression fold change was first calculated in cancer tissue compared to its normal counter and then compared between low and high MTV groups to obtain significant DEGs. To assess the suitability of the DEGs for clinical application, the correlation of the DEGs with tumor grades and clinical outcomes was analyzed in TCGA-PAAD, a large dataset without MTV information.ResultsTotal RNA-sequencing (MTV RNA-Seq) revealed that 44 genes were upregulated and 56 were downregulated in the high MTV group. We selected the 29 genes matching MTV RNA-seq patterns in the TCGA-PAAD dataset, a large clinical dataset without MTV information, as MTV-associated genes (MAGs). In the analysis with the TCGA dataset, MAGs were significantly associated with patient survival, treatment outcomes, TCGA-PAAD-suggested markers, and CEACAM family proteins. Some MAGs showed an inverse correlation with miRNAs and were confirmed to be differentially expressed between normal and cancerous pancreatic tissues. Overexpression of KIF11 and RCC1 and underexpression of ADCY1 and SDK1 were detected in ~ 60% of grade 2 pancreatic cancer patients and associated with ~ 60% mortality in stages I and II.ConclusionsMAGs may serve as diagnostic markers and miRNA therapeutic targets for pancreatic cancer. Among the MAGs, KIF11, RCC1, ADCY, and SDK1 may be early diagnostic markers.

An early-stage Alzheimer's disease detection using various imaging modalities and techniques – A mini-review

An early-stage Alzheimer's disease detection using various imaging modalities and techniques – A mini-review

PDSMNet: Parallel pyramid dual-stream modeling for automatic lung COVID-19 infection segmentations

Artificial intelligence-based segmentation models can assist the early-stage detection of lung COVID-19 infections or lesions from medical images with higher efficiency versus traditional techniques, yet challenges remain attributable to factors such as heterogeneities in infection traits, small infection regions, blurred boundary context, mixtures of varying infection regions, and obscure intensity contrast between lesions and normal tissues. This study aims to improve the performance of automatic segmentation of lung COVID-19 infections by proposing a novel approach named parallel pyramid dual-stream modeling network (PDSMNet) with two major contributions: (1) refined design of the framework including (a) a transformer module termed parallel pyramid dual-stream module (PDSM) to effectively preserve channel, spatial, and other latent features; (b) fusion of a multi-scale pyramid parallel-pooling module (MPM) that extracts features in parallel at differentiated scales; (c) calibration of the skip-connection architectures to optimize prediction and preserve features; (d) calibration of attention mechanisms reflecting multiple sources of information including parallel-, serial-, and cross-attention contexts; and (e) improvement of the integral functionality of the framework in curtailing the burdens of parameter computations in a multi-modality scenario. (2) loss functions accounting for the training losses of normal tissues, diseases, and boundaries respectively to enhance the performance of the network. The calibrated loss design allowing for a margin improves the capacity of predictions. We conducted experiments using three different datasets with different modalities and compared the proposed framework PDSMNet with two other benchmarks and eight similar state-of-the-art (SOTA) networks. The experiments observed consistent performance improvements across all datasets. PDSMNet attained asymptotically a maximum increase of 16.5%, 6.2%, and 15.5% for mean F1 score (mF1S), mean dice-score coefficient (mDSC), mean intersection over union (mIoU) versus SOTA PDEAtt-UNet, a maximum increase of 49.6%, 25.6%, and 38.0% for mF1S, mDSC, and mIoU versus InfNet, a maximum increase of 26.1%, 10.8%, 21.8% versus MiniSeg, a maximum increase of 14.0%, 3.0%, and 13.4% versus TransUNet, and a maximum increase of 37.9%, 16.8%, 31.2% versus Attention-UNet respectively. For other SOTA models such as MT-UNet, UCTransNet, and UTNetV2, a sizeable enhancement of performance was observed as well. PDSMNet also yielded asymptotically a maximum 30.6%, 14.8%, 26.3% increase of mF1S, mDSC, mIoU versus benchmark UNet, and a maximum increase of 42.8%, 21.5%, 33.3% versus benchmark UNet++ respectively. PDSMNet demonstrated reduced computational costs as well, yielding approximately 0.53 M of parameters and 6.55G of floating-point operations per second (FLOPs) respectively using different datasets, and the quantitative and qualitative ablation tests reinforced the effectiveness of the various components of the proposed framework.

The Future Role of Abdominal US in Hepatocellular Carcinoma Surveillance.

Abdominal US is currently the best-validated surveillance strategy for hepatocellular carcinoma (HCC) in at-risk patients. It is the only modality shown to have completed all five phases of validation and can achieve high sensitivity and specificity for HCC detection, especially when conducted by expert sonographers in high-volume centers. However, US also has limitations, including operator dependency and varying sensitivity in clinical practice. Further, the sensitivity of US for early-stage HCC detection is lower in patients with obesity or nonviral liver disease, increasingly common populations undergoing surveillance. Imaging-based and blood-based surveillance strategies, including abbreviated MRI and biomarker panels, may overcome some limitations of US-based surveillance. Both strategies have promising test performance in phase II and phase III biomarker studies and are undergoing prospective validation. Considering the variation in HCC risk and test performance between patients, there will likely be a shift away from a one-size-fits-all approach and toward precision screening, in which the "best" test is selected based on individual patient characteristics. In this upcoming era of precision HCC screening among patients with cirrhosis, US will likely continue to have an important, albeit reduced, surveillance role.

Early-stage biomarkers identification by integrated genomic analysis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC), a type of liver cancer, ranks as the third-leading cause of death due to the lack of definite biomarkers for early-stage detection. HCC progression occurs by the dysregulation of several genes. Though several studies focus on biomarkers for HCC diagnosis, stage-specific marker identification remains elusive. Hence, the present study aims to identify early-stage biomarkers for the detection of HCC through integrated in silico analysis. The differential gene expression was performed using GEO2R for the datasets (GSE14520, GSE63898, GSE121248, GSE124535, GSE94660, and GSE6764) retrieved from Gene Expression Omnibus (GEO) of patients with cirrhotic liver or HCC. The common differentially expressed gene enrichment analysis was performed using Funrich for Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomics (KEGG) gene mapping. The Protein-Protein Interaction (PPI) network was performed using the Search Tool for the Retrieval of Interacting Genes (STRING). The hub genes were identified using the CytoHubba plug-in of Cytoscape software. The identified genes were verified for their prognostic value using the Kaplan-Meier plotter and Immunohistochemistry micrographs obtained from the Human Protein Atlas database. An overall of 243 common differentially expressed genes (DEGs) were identified containing 171 upregulated and 72 downregulated genes. With the help of PPI network construction, ten hub genes were identified as CDK1, AURKA, CCNB1, CCNB2, CENPF, CDC20, TOP2A, BUB1, RRM2, and HMMR, which are dysregulated owing to HCC proliferation, tumorigenesis and poor prognosis in patients. These hub genes are suitable waypoints for the diagnosis and targeted therapy against early-stage HCC.

Early-Stage ISC Fault Detection for Ship Lithium Batteries Based on Voltage Variance Analysis

With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a series of safety problems to ship operations, especially the battery internal short circuit (ISC). Battery ISC faults are very hidden and unpredictable at the initial stage and often fail to be detected in time, ultimately leading to overheating, fire or even an explosion of the ship’s power system. Based on this, this paper proposes a fast and accurate method for early-stage ISC fault location and detection of lithium batteries. Initially, voltage variations across the lithium battery packs are quantified using curvilinear Manhattan distances to pinpoint faulty battery units. Subsequently, the localized characteristics of voltage variance among adjacent batteries are leveraged to detect an early-stage ISC fault. Simulation results indicate that the proposed method can quickly and accurately locate the position of 5 Ω, 10 Ω and 15 Ω ISC faulty batteries within the battery pack, as well as detect the abnormal batteries in a timely manner with considerable sensitivity and reliability.

A Machine Learning Approach for Early-Stage Autism Spectrum Disorders Detection

Abstract: The undertaking proposes a Machine Learning (ML) system for early ID of Autism Spectrum illness (ASD), recognizing the hardships of taking out the sickness yet endeavoring to diminish its seriousness through early therapies. The proposed system tests four Feature Scaling (FS) methods (Quantile Transformer, Power Transformer, Normalizer, Max Abs Scaler) on four normal ASD datasets from babies to grown-ups. ML calculations (e.g., Ada Boost, Random Forest, Decision Tree, K-Nearest Neighbors, Gaussian Naïve Bayes, Logistic Regression, SVM, LDA) are utilized on included scaled datasets. The best classifiers and FS strategies for each age bunch are recognized utilizing factual estimations. The voting classifier predicts ASD with the greatest accuracy for Babies, Kids, Young people, and Grown-ups. The task incorporates a definite element significance examination utilizing four Component Determination Strategies to underline the significance of calibrating ML techniques in foreseeing ASD across age gatherings and to assist medical services specialists with pursuing ASD screening choices. In contrast with current early ASD discovery strategies, the proposed structure performs well. To further develop ASD identification strength and precision, a group procedure using a Voting Classifier with Random Forest (RF) and AdaBoost achieved 100% accuracy.

Ganoderma lucidum: Insights on host range, diagnosis, and management strategies.

Forest ecosystems play an important role in upholding life on our planet. However, the onslaught of fungal pathogens like Ganoderma lucidum, poses a threat by decimating numerous tree species. G. lucidum identified as a root pathogen, causing root rot in numerous tree species of horticulture and forestry importance. The fungus initiates infection through basidiospores, which germinate and penetrate within roots and start to degrade lignocellulosic components of plant cells. Early-stage detection of G. lucidum, is challenging, while in advance stages, the wood undergoes softening and a loss of tensile strength, rendering the disease incurable. Hence, effective management of G. lucidum necessitates a pivotal role of disease diagnostic techniques, which are currently underutilized or inadequately accessible. Subsequent implementation of suitable control measures becomes imperative to thwart disease occurrence and mitigate its impact in early stages, thus preserving the vitality of forest ecosystems. This study provides comprehensive overview of G. lucidum, covering taxonomy, pathogenicity, disease cycle, diagnosis and effective control measures, which will be helpful in formulating effective diagnostic techniques for early management of root rot disease.