Screening Method Research Articles

Colorectal cancer screening with virtual colonography

Since 2003, adecline in the age-standardized incidence rates of colorectal cancer (CRC) has been observed in Germany. Nonetheless, one in eight cancer cases still affects the colon or rectum. The prognosis has improved, with the relative 5‑year survival rate for CRC being approximately 65%. This positive trend is probably aresult of preventive measures introduced over the last 20years. This could be further improved, however, as CRC can not only be detected early but in almost all cases also prevented through the identification of benign precursors. Less than half of all eligible individuals participate in screening via colonoscopy. This implies that further, possibly even imaging, screening test methods should be explored and offered. Studies have reported that virtual colonography techniques have acomparable accuracy to endoscopy of about 90% for polyp sizes larger than 5 mm. The data for computed tomography (CT) is more extensive than for magnetic resonance imaging (MRI). Significant challenges are posed however by the fact that in Germany CT colonography (CTC) is not considered aviable screening option due to radiation protection concerns, and MRI screening is not an established screening method. Radiologists should be familiar with classification using the CT Colonography Reporting and Data System (C-RADS), which uses criteria such as CT density, morphology, size, and location for classification. C‑RADS classification follows the categories: C0 (inadequate study), C1 (normal), C2a (indeterminate), C2b (benign), C3 (suspicious), and C4 (malignant), as well as extracolonic categories E1/2 (no clinically significant findings), E3 (likely insignificant findings), and E4 (likely significant findings).

Screening Methods for Cervical Cancer.

Cervical cancer seriously affects the health of women worldwide. Persistent infection of high-risk HPV (Human Papilloma Virus) can lead to cervical cancer. There is a great need for timely and efficient screening methods for cervical cancer. The current screening methods for cervical cancer are mainly based on cervical cytology and HPV testing. Cervical cytology is made of Pap smear and liquid-based cytology,while HPV testing is based on immunological and nucleic acid level detection methods. This review introduces cervical cancer screening methods based on cytology and human papillomavirus testing in detail. The advantages and limitations of the screening methods are also summarized and compared.

Unveiling the emerging role of curcumin to alleviate ochratoxin A-induced muscle toxicity in grass carp (Ctenopharyngodon idella): in vitro and in vivo studies

BackgroundOchratoxin A (OTA), a globally abundant and extremely hazardous pollutant, is a significant source of contamination in aquafeeds and is responsible for severe food pollution. The developmental toxicity of OTA and the potential relieving strategy of natural products remain unclear. This study screened the substance curcumin (Cur), which had the best effect in alleviating OTA inhibition of myoblast proliferation, from 96 natural products and investigated its effect and mechanism in reducing OTA myotoxicity in vivo and in vitro.MethodsA total of 720 healthy juvenile grass carp, with an initial average body weight of 11.06 ± 0.05 g, were randomly assigned into 4 groups: the control group (without OTA and Cur), 1.2 mg/kg OTA group, 400 mg/kg Cur group, and 1.2 mg/kg OTA + 400 mg/kg Cur group. Each treatment consisted of 3 replicates (180 fish) for 60 d.ResultsFirstly, we cultured, purified, and identified myoblasts using the tissue block culture method. Through preliminary screening and re-screening of 96 substances, we examined cell proliferation-related indicators such as cell viability and ultimately found that Cur had the best effect. Secondly, Cur could alleviate OTA-inhibited myoblast differentiation and myofibrillar development-related proteins (MyoG and MYHC) in vivo and in vitro and improve the growth performance of grass carp. Then, Cur could also promote the expression of OTA-inhibited protein synthesis-related proteins (S6K1 and TOR), which was related to the activation of the AKT/TOR signaling pathway. Finally, Cur could downregulate the expression of OTA-enhanced protein degradation-related genes (murf1, foxo3a, and ub), which was related to the inhibition of the FoxO3a signaling pathway.ConclusionsIn summary, our data demonstrated the effectiveness of Cur in alleviating OTA myotoxicity in vivo and in vitro. This study confirms the rapidity, feasibility, and effectiveness of establishing a natural product screening method targeting myoblasts to alleviate fungal toxin toxicity.Graphical

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models

Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.

Flaxseeds Composition And Phytochemical Screening Methods

Flaxseed, often known as linseed (Linum usitatissimum L.), is derived from the annual flax plant. The human nutrition industry is the primary application for flaxseed, as it is becoming recognized as a significant functional food component because to the active ingredient content, which is shown to have health advantages. Flaxseed can be consumed in a variety of forms, such as ground, as oil, or combined with baked goods. Flaxseed consumption is supported by scientific evidence due to its high content of omega-3 and omega-6 rich oil, lignans, α-linolenic acid, high-quality proteins, and fibers. These compounds are biologically active and can prevent various chronic diseases like diabetes, cardiovascular disease, cerebrovascular stroke, and many types of cancer. Additionally, benefits from flaxseed diet have been demonstrated in the field of animal nutrition, and as a result in healthier food from animal origin. In fact, the fatty acid profile of the meat and fat is directly affected by the source of fat in diet in swine and poultry, feeding omega-3 enriched diets by the addition of flaxseed would increase the omega-3 content in eggs and meat and thus enrich the products.

Screening of aspiration pneumonia using the modified Mallampati classification tool in older adults.

Pneumonia is a major cause of morbidity and mortality in older adults. In the aging society, screening methods for predicting aspiration pneumonia are crucial for its prevention. Changes in the oropharyngeal morphology and hyoid bone position may increase the risk of aspiration pneumonia. This multicenter study aimed to investigate a simple and effective screening method for predicting dysphagia and aspiration pneumonia. Overall, 191 older adults (aged 65 years or older) were randomly sampled using the simple random sampling technique. Oropharyngeal morphology was assessed using the modified Mallampati classification, which reflects the size of the tongue in the oropharyngeal cavity. The hyoid position was measured as the distance between the menton and laryngeal prominence to evaluate aging-related changes in the muscles of the laryngopharynx. Dysphagia was assessed using the repetitive saliva swallowing test (RSST), which measures the number of swallowing movements in 30 seconds; dysphasia is defined as less than 3 swallowing movements in 30 seconds. The aspiration signs were assessed based on history of choking or coughing reflex during eating or drinking and medical history of pneumonia. The study findings revealed that the modified Mallampati classification was significantly correlated with a medical history of pneumonia. A higher incidence of pneumonia was evident in the lower Mallampati classification, which shows the smaller size of the tongue base in the oropharyngeal cavity. The results of this study suggest that the modified Mallampati classification may be a possible screening method to predict the occurrence of pneumonia.

Transcriptionally Active Human Papillomavirus Infection in a Minority of Esophageal Squamous Cell Carcinomas in North America.

The role of Human papillomavirus (HPV) infection in esophageal squamous cell carcinoma (ESCC) is a topic of ongoing debate. This study used two screening approaches to look for evidence of HPV infection in esophageal squamous cell carcinoma. We initially checked for HPV infection in a randomly selected group of 53 ESCC cases. We did not detect any tumors positive for high-risk HPV. However, during clinical practice, we identified an HPV-positive ESCC in the distal esophagus, which tested positive for HPV16. This index case was TP53 wild-type, as determined by next-generation DNA sequencing (NGS). Since TP53 mutations are rare in other HPV-driven cancers, we improved our screening method by limiting our screen to a subset of ESCC cases without TP53 mutations. A second screen of 95 ESCCs (from 93 patients) sequenced by NGS revealed an additional 7 ESCCs with TP53 wild-type status (7.3% of the total). Of the 7 cases, 2 cases were found to be high-risk HPV positive. Both patients also tested positive for circulating cell-free HPV DNA and had a complete response to neoadjuvant chemoradiation. The index patient had microscopic residual tumor following neoadjuvant therapy. The patient underwent adjuvant immunotherapy and remained disease free after 22 months of surveillance. This study affirms the transcriptionally active status of high-risk HPV in a minority of ESCC patients in North America.

Exploring the Role of Mindfulness Meditation in Facilitating English Learning and Optimizing Classroom Teaching

Mindfulness meditation is a technique for regulating attention and emotions, which can help individuals enhance self-awareness and reduce stress. This paper aims at review the positive effects of mindfulness meditation on English learners and teachers, including improving English learning abilities and enhancing motivation, confidence, and enjoyment in English learning. Through systematic literature searches and screening methods from databases such as CNKI, Wan fang, Pub Med, and Web of Science, relevant papers on mindfulness meditation and English learning from the past decade were selected. Their research purposes, methods, innovations, and conclusions were analyzed. This paper specifically explores the literature review on the impact of mindfulness meditation on English learning, summarizing both the theory and practice of mindfulness meditation, and proposing future research directions and suggestions. Mindfulness meditation has a positive impact on English learning and classroom teaching, but its specific effects are influenced by various factors such as the type, duration, and frequency of mindfulness meditation. More empirical research is needed to validate its effects on English learning and to explore the optimal integration methods.

The incidence and risk factors of retinopathy of prematurity in South Korea: A nationwide cohort study.

Retinopathy of prematurity (ROP) is a major treatable cause of childhood blindness. Thus, epidemiological investigations are necessary for detecting and preventing ROP. Determining risk factors for ROP are also essential to improve screening methods. Therefore, we aimed to investigate the incidence and risk factors of ROP in Korea. The National Health Insurance Service (NHIS) covers almost all Koreans. Furthermore, the National Health Screening Program for Infants and Children (NHSPIC) is a government-run, health-screening program for children aged < 6 years. We used the NHIS-Infants and Children's Health Screening cohort database to evaluate the incidence of preterm infants and ROP. The database contains data on 84,005 participants, drawn from 5% of the NHSPIC survey on participants born annually during 2008 to 2012. Sociodemographic factors and systemic diseases were assessed as potential risk factors for ROP. We identified 2615 premature infants (3.11%); 846 of them had ROP (cumulative incidence: 32.4%). Although preterm births increased annually in 2008 to 2012, the ROP incidence in preterm infants did not increase by the birth year. Twenty patients (2.4%) with ROP underwent laser photocoagulation or surgery. Extremely low birth weight was a high risk factor (odds ratio [OR] = 49.86, P < .001). Moreover, chorioamnionitis (OR = 2.77, P = .028), respiratory distress syndrome (OR = 4.09, P < .001), apnea (OR = 1.59, P = .008), anemia (OR = 2.41, P < .001), and intraventricular hemorrhage (OR = 2.34, P < .001) were found to be risk factors for ROP. In conclusion, the incidence of premature babies increased between 2008 and 2012. However, the overall incidence of ROP among premature infants remained unchanged by birth year. Our findings revealed the roles of birth weight, respiratory conditions, anemia, and intraventricular hemorrhage in ROP.

A narrative review: suicide and suicidal behaviour in older adults

Globally, suicide is a public health concern that claims the lives of many each year. The complex etiology and factors contributing to the risk of suicide make it hard to predict the likelihood of death by suicide. Suicide rates have been increasing over the past 25 years in patients aged 65 years and older, and with the expected increases in the size of the older adult population and the under-detection of suicide risk, these rates may continue to increase. To mitigate and attempt to limit this expected increase, it is important to understand the risk and protective factors of suicide in older adults. This narrative review focuses on individuals above the age of 65 and encompasses relevant peer-reviewed publications from the past 25 years to cover fatal and non-fatal suicidal behaviour. It summarizes several important risk factors for suicide and suicidal behaviors while considering how risk can be detected, assessed, prevented, and mitigated. Screening methods to detect suicide and depression in older adults were examined based on their effectiveness and suitability for use in this population. Lastly, the impacts of the COVID-19 pandemic on suicide rates in older adults were described.

Development, validation and application of single molecule molecular inversion probe based novel integrated genetic screening method for 29 common lysosomal storage disorders in India

BackgroundCurrent clinical diagnosis pathway for lysosomal storage disorders (LSDs) involves sequential biochemical enzymatic tests followed by DNA sequencing, which is iterative, has low diagnostic yield and is costly due to overlapping clinical presentations. Here, we describe a novel low-cost and high-throughput sequencing assay using single-molecule molecular inversion probes (smMIPs) to screen for causative single nucleotide variants (SNVs) and copy number variants (CNVs) in genes associated with 29 common LSDs in India.Results903 smMIPs were designed to target exon and exon–intron boundaries of targeted genes (n = 23; 53.7 kb of the human genome) and were equimolarly pooled to create a sequencing library. After extensive validation in a cohort of 50 patients, we screened 300 patients with either biochemical diagnosis (n = 187) or clinical suspicion (n = 113) of LSDs. A diagnostic yield of 83.4% was observed in patients with prior biochemical diagnosis of LSD. Furthermore, diagnostic yield of 73.9% (n = 54/73) was observed in patients with high clinical suspicion of LSD in contrast with 2.4% (n = 1/40) in patients with low clinical suspicion of LSD. In addition to detecting SNVs, the assay could detect single and multi-exon copy number variants with high confidence. Critically, Niemann-Pick disease type C and neuronal ceroid lipofuscinosis-6 diseases for which biochemical testing is unavailable, could be diagnosed using our assay. Lastly, we observed a non-inferior performance of the assay in DNA extracted from dried blood spots in comparison with whole blood.ConclusionWe developed a flexible and scalable assay to reliably detect genetic causes of 29 common LSDs in India. The assay consolidates the detection of multiple variant types in multiple sample types while having improved diagnostic yield at same or lower cost compared to current clinical paradigm.

The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations

Objectives18F-fluorodeoxyglucose (FDG) PET/CT has been widely used for the differential diagnosis of cancer. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may make it difficult to differentiate between benign and malignant lesions. It is crucial to find reliable quantitative metabolic parameters to further support the diagnosis. This study aims to evaluate the value of the quantitative metabolic parameters derived from dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status.MethodsWe included 147 patients with lung lesions to perform FDG PET/CT dynamic plus static imaging with informed consent. Based on the results of the postoperative pathology, the patients were divided into benign/malignant groups, adenocarcinoma (AC)/squamous carcinoma (SCC) groups, and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Quantitative parameters including K1, k2, k3, and Ki of each lesion were obtained by applying the irreversible two-tissue compartmental modeling using an in-house Matlab software. The SUV analysis was performed based on conventional static scan data. Differences in each metabolic parameter among the group were analyzed. Wilcoxon rank-sum test, independent-samples T-test, and receiver-operating characteristic (ROC) analysis were performed to compare the diagnostic effects among the differentiated groups. P < 0.05 were considered statistically significant for all statistical tests.ResultsIn the malignant group (N = 124), the SUVmax, k2, k3, and Ki were higher than the benign group (N = 23), and all had-better performance in the differential diagnosis (P < 0.05, respectively). In the AC group (N = 88), the SUVmax, k3, and Ki were lower than in the SCC group, and such differences were statistically significant (P < 0.05, respectively). For ROC analysis, Ki with cut-off value of 0.0250 ml/g/min has better diagnostic specificity than SUVmax (AUC = 0.999 vs. 0.70). In AC group, 48 patients further underwent EGFR testing. In the EGFR (+) group (N = 31), the average Ki (0.0279 ± 0.0153 ml/g/min) was lower than EGFR (-) group (N = 17, 0.0405 ± 0.0199 ml/g/min), and the difference was significant (P < 0.05). However, SUVmax and k3 did not show such a difference between EGFR (+) and EGFR (-) groups (P>0.05, respectively). For ROC analysis, the Ki had a cut-off value of 0.0350 ml/g/min when predicting EGFR status, with a sensitivity of 0.710, a specificity of 0.588, and an AUC of 0.674 [0.523–0.802].ConclusionAlthough both techniques were specific, Ki had a greater specificity than SUVmax when the cut-off value was set at 0.0250 ml/g/min for the differential diagnosis of lung cancer. At a cut-off value of 0.0350 ml/g/min, there was a 0.710 sensitivity for EGFR status prediction. If EGFR testing is not available for a patient, dynamic imaging could be a valuable non-invasive screening method.

FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data—Study protocol of a multicenter trial

Introduction Endometriosis is a chronic disease that affects up to 190 million women and those assigned female at birth and remains unresolved mainly in terms of etiology and optimal therapy. It is defined by the presence of endometrium-like tissue outside the uterine cavity and is commonly associated with chronic pelvic pain, infertility, and decreased quality of life. Despite the availability of various screening methods (e.g., biomarkers, genomic analysis, imaging techniques) intended to replace the need for invasive surgery, the time to diagnosis remains in the range of 4 to 11 years. Aims This study aims to create a large prospective data bank using the Lucy mobile health application (Lucy app) and analyze patient profiles and structured clinical data. In addition, we will investigate the association of removed or restricted dietary components with quality of life, pain, and central pain sensitization. Methods A baseline and a longitudinal questionnaire in the Lucy app collects real-world, self-reported information on symptoms of endometriosis, socio-demographics, mental and physical health, economic factors, nutritional, and other lifestyle factors. 5,000 women with confirmed endometriosis and 5,000 women without diagnosed endometriosis in a control group will be enrolled and followed up for one year. With this information, any connections between recorded symptoms and endometriosis will be analyzed using machine learning. Conclusions We aim to develop a phenotypic description of women with endometriosis by linking the collected data with existing registry-based information on endometriosis diagnosis, healthcare utilization, and big data approach. This may help to achieve earlier detection of endometriosis with pelvic pain and significantly reduce the current diagnostic delay. Additionally, we may identify dietary components that worsen the quality of life and pain in women with endometriosis, upon which we can create real-world data-based nutritional recommendations.

End-to-End Semi-Supervised Opportunistic Osteoporosis Screening Using Computed Tomography.

Osteoporosis is the most common metabolic bone disease and can cause fragility fractures. Despite this, screening utilization rates for osteoporosis remain low among populations at risk. Automated bone mineral density (BMD) estimation using computed tomography (CT) can help bridge this gap and serve as an alternative screening method to dual-energy X-ray absorptiometry (DXA). The feasibility of an opportunistic and population agnostic screening method for osteoporosis using abdominal CT scans without bone densitometry phantom-based calibration was investigated in this retrospective study. A total of 268 abdominal CT-DXA pairs and 99 abdominal CT studies without DXA scores were obtained from an oncology specialty clinic in the Republic of Korea. The center axial CT slices from the L1, L2, L3, and L4 lumbar vertebrae were annotated with the CT slice level and spine segmentation labels for each subject. Deep learning models were trained to localize the center axial slice from the CT scan of the torso, segment the vertebral bone, and estimate BMD for the top four lumbar vertebrae. Automated vertebra-level DXA measurements showed a mean absolute error (MAE) of 0.079, Pearson's r of 0.852 (P<0.001), and R2 of 0.714. Subject-level predictions on the held-out test set had a MAE of 0.066, Pearson's r of 0.907 (P<0.001), and R2 of 0.781. CT scans collected during routine examinations without bone densitometry calibration can be used to generate DXA BMD predictions.

SCREENING AND EARLY DETECTION OF LUNG CANCER: A COMPREHENSIVE SYSTEMATIC REVIEW

Background: Lung cancer is the second most commonly diagnosed cancer and the leading cause of cancer-related deaths globally, accounting for the greatest economic and public health burden of all cancers. In 2020, lung cancer accounted for approximately 2.2 million cases and nearly 1.8 million deaths worldwide. The aim: The aim of this study to show about screening and early detection of lung cancer. Methods: By the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. This search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed, SagePub, and Sciencedirect were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: Four publications were found to be directly related to our ongoing systematic examination after a rigorous three-level screening approach. Subsequently, a comprehensive analysis of the complete text was conducted, and additional scrutiny was given to these articles. Conclusion: Early screening and diagnostic methods of lung cancer, x-ray screening is not recommended. LDCT has obvious advantages and is the most promising imaging method in early screening of lung cancer.

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

BackgroundIn the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adults and investigate whether surface electromyogram (sEMG) from hand grip could potentially be used to detect sarcopenia using machine learning (ML) methods with reasonable features extracted from sEMG signals. The secondary aim was to provide the interpretability of the obtained ML models using a novel feature importance estimation method.MethodsA total of 158 community-dwelling older residents (≥ 60 years old) were recruited. After screening through the diagnostic criteria of the Asian Working Group for Sarcopenia in 2019 (AWGS 2019) and data quality check, participants were assigned to the healthy group (n = 45) and the sarcopenic group (n = 48). sEMG signals from six forearm muscles were recorded during the hand grip task at 20% maximal voluntary contraction (MVC) and 50% MVC. After filtering recorded signals, nine representative features were extracted, including six time-domain features plus three time-frequency domain features. Then, a voting classifier ensembled by a support vector machine (SVM), a random forest (RF), and a gradient boosting machine (GBM) was implemented to classify healthy versus sarcopenic participants. Finally, the SHapley Additive exPlanations (SHAP) method was utilized to investigate feature importance during classification.ResultsSeven out of the nine features exhibited statistically significant differences between healthy and sarcopenic participants in both 20% and 50% MVC tests. Using these features, the voting classifier achieved 80% sensitivity and 73% accuracy through a five-fold cross-validation. Such performance was better than each of the SVM, RF, and GBM models alone. Lastly, SHAP results revealed that the wavelength (WL) and the kurtosis of continuous wavelet transform coefficients (CWT_kurtosis) had the highest feature impact scores.ConclusionThis study proposed a method for community-based sarcopenia screening using sEMG signals of forearm muscles. Using a voting classifier with nine representative features, the accuracy exceeds 70% and the sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.

In silico identification of a novel Cdc2‐like kinase 2 (CLK2) inhibitor in triple negative breast cancer

AbstractDysregulation of RNA splicing processes is intricately linked to tumorigenesis in various cancers, especially breast cancer. Cdc2‐like kinase 2 (CLK2), an oncogenic RNA‐splicing kinase pivotal in breast cancer, plays a significant role, particularly in the context of triple‐negative breast cancer (TNBC), a subtype marked by substantial medical challenges due to its low survival rates. In this study, we employed a structure‐based virtual screening (SBVS) method to identify potential CLK2 inhibitors with novel chemical structures for treating TNBC. Compound 670551 emerged as a novel CLK2 inhibitor with a 50% inhibitory concentration (IC50) value of 619.7 nM. Importantly, Compound 670551 exhibited high selectivity for CLK2 over other protein kinases. Functionally, this compound significantly reduced the survival and proliferation of TNBC cells. Results from a cell‐based assay demonstrated that this inhibitor led to a decrease in RNA splicing proteins, such as SRSF4 and SRSF6, resulting in cell apoptosis. In summary, we identified a novel CLK2 inhibitor as a promising potential treatment for TNBC therapy.

E-CLEAP: An ensemble learning model for efficient and accurate identification of antimicrobial peptides.

With the increasing problem of antimicrobial drug resistance, the search for new antimicrobial agents has become a crucial task in the field of medicine. Antimicrobial peptides, as a class of naturally occurring antimicrobial agents, possess broad-spectrum antimicrobial activity and lower risk of resistance development. However, traditional screening methods for antimicrobial peptides are inefficient, necessitating the development of an efficient screening model. In this study, we aimed to develop an ensemble learning model for the identification of antimicrobial peptides, named E-CLEAP, based on the Multilayer Perceptron Classifier (MLP Classifier). By considering multiple features, including amino acid composition (AAC) and pseudo amino acid composition (PseAAC) of antimicrobial peptides, we aimed to improve the accuracy and generalization ability of the identification process. To validate the superiority of our model, we employed five-fold cross-validation and compared it with other commonly used methods for antimicrobial peptide identification. In the experimental results on an independent test set, E-CLEAP achieved accuracies of 97.33% and 84% for the AAC and PseAAC features, respectively. The results demonstrated that our model outperformed other methods in all evaluation metrics. The findings of this study highlight the potential of the E-CLEAP model in enhancing the efficiency and accuracy of antimicrobial peptide screening, which holds significant implications for drug development, disease treatment, and biotechnology advancement. Future research can further optimize the model by incorporating additional features and information, as well as validating its reliability on larger datasets and in real-world environments. The source code and all datasets are publicly available at https://github.com/Wangsicheng52/E-CLEAP.

Knowledge and Level of Awareness Regarding Breast Cancer and Practices of Breast Screening Methods Among Female Riyadh Citizens

Knowledge and Level of Awareness Regarding Breast Cancer and Practices of Breast Screening Methods Among Female Riyadh Citizens

Alpha/beta internal contamination rapid screening methods for radiation emergency

Alpha/beta internal contamination rapid screening methods for radiation emergency