Early Screening Methods Research Articles

DNA‐Gated N‐CDs@SiO2 Nanoparticles‐Based Biosensor for MUC1 Detection

AbstractThe ultrasensitive detection of mucin1 (MUC1), a typical breast cancer marker, is of great significance for the diagnosis of breast cancer. Here, a biosensor based on DNA‐gated nitrogen‐doped carbon quantum dots‐loaded hollow mesoporous silica nanoparticles (DNA‐gated N‐CDs@SiO2 NPs) for highly sensitive detection of MUC1 were reported. The preparation process and properties of DNA‐gated N‐CDs@SiO2 NPs were characterized in detail. Results of experiments of detection of MUC1 and cell imaging indicate that the biosensor with MUC1‐specific DNA sequence is beneficial for selective detection of MUC1 by MUC1‐triggered unlocking of DNA gate and controlled release of N‐CDs. The biosensor exhibits satisfactory results with a low detection limit of 10 nM and good specificity, as well as MUC1‐positive cell imaging performance, demonstrating the potential for MUC1 detection, which provides a new method for early screening and diagnosis of breast cancer diseases.

D-dimer level significance for deep vein thrombosis screening in the third trimester: a retrospective study

BackgroundVenous thromboembolism often develops after surgery and childbirth, resulting in death in some cases. Although early deep vein thrombosis (DVT) detection can predict pulmonary thromboembolism, there is no early screening method for DVT in pregnant women. Lack of consensus regarding significance or setting and cut-off value interpretation of D-dimer levels further impedes venous thromboembolism screening in pregnant women.This study aimed to examine the utility of third-trimester serum D-dimer levels as a screening test for DVT during pregnancy and to determine the frequency of asymptomatic DVT using lower-limb compression ultrasonography.MethodsThis single-center retrospective study included 497 pregnant women who underwent elective cesarean section at term in our hospital between January 2013 and December 2019. Serum D-dimer levels were preoperatively measured at 32–37 weeks’ gestation. The presence or absence of DVT in patients with serum D-dimer levels ≥ 3.0 µg/ml, the cut-off value, was examined using compression ultrasonography. In all patients, the presence or absence of clinical venous thrombosis (symptoms such as lower-limb pain, swelling, and heat sensation) was examined within 4 postoperative weeks.The Royal College of Obstetricians and Gynecologists Guideline 2015 was referred to determine risk factors for the onset of venous thrombosis during pregnancy. Among those, we examined the risk factors for DVT that result in high D-dimer levels during pregnancy.ResultsThe median age and body mass index were 35 (20–47) years and 21.2 (16.4–41.1) kg/m2, respectively. Further, the median gestational age and D-dimer levels were 37 weeks and 2.1 (0.2–16.0) µg/ml, respectively. Compression ultrasonography was performed on 135 (26.5%) patients with a D-dimer level ≥ 3.0 µg/ml, with none of the patients showing DVT. All patients were followed up for 4 postoperative weeks, with none presenting with venous thromboembolism. Multivariate analysis showed that hypertensive disorders of pregnancy are an independent risk factor for venous thromboembolism that causes high D-dimer levels (odds ratio: 2.48, 95% confidence interval: 1.05–6.50, P = 0.04).ConclusionThere may be low utility in screening for DVT using D-dimer levels in the third trimester. Further, prepartum asymptomatic DVT has a low frequency, indicating the low utility of compression ultrasonography.Trial registrationInstitutional Review Board of Tottori University Hospital (IRB no. 20A149).

Comparative assessment of umbilical cord blood with peripheral venous blood using hematological scoring system as an early predictive screening method for the detection of early-onset neonatal sepsis in the tertiary care center of Central India

Background: Sepsis is one of the major causes of neonatal morbidity and mortality. Early recognition and diagnosis of early-onset neonatal sepsis (EONS) is difficult because of the variable and non-specific clinical presentation of this condition. Hence, there is a need for early predictive screening method for EONS. Aims and Objectives: To compare the umbilical cord blood Haematological Scoring System (HSS) with peripheral venous blood as an early predictive screening method for detection of EONS. Materials and Methods: 100 inborn neonates with two or more risk factors for EONS, chosen by sequential sampling method were included in this prospective analytical study. Blood samples were collected from the umbilical cord and peripheral vein analyzed for hematological parameters, sepsis screen, and peripheral smear for HSS of Rodwell et al., send for blood culture. Blood cultures were performed as gold standard for diagnosing neonatal sepsis and sepsis screen was done to corroborate the diagnosis of neonatal sepsis. Results: Of 100 neonates, 21 belongs to sepsis; 14 to probable sepsis; 65 to no sepsis. HSS in umbilical cord blood (UCB) had Sensitivity-74.28%, Specificity-92.30%, PPV-83.87%, NPV-86.95% and HSS in PVB had Sensitivity-62.85%, Specificity- 87.69%, PPV-75.86%, NPV-81.69%. Conclusion: HSS score in UCB can be used as a simple, quick, cost-effective, and readily available screening test with decent sensitivity and high specificity, for the detection of EONS.

Arterial stiffness measurements in pregnancy as a predictive tool for hypertensive disorders of pregnancy and preeclampsia: Protocol for a systematic review and meta-analysis.

Hypertensive disorders of pregnancy (HDPs) are a leading cause of maternal morbidity and mortality worldwide. Unfortunately, accurate early clinical screening methods for the development of these disorders are lacking. Arterial stiffness (AS) is an important hemodynamic indicator of vascular health that has shown promising results for the prediction of HDP onset. Past systematic reviews in the field have reported an increase in AS indices in women who develop HDPs and have highlighted the potential of AS measurements as a predictive tool early in pregnancy. The most recent systematic review, including papers up to 2015, assessed the differences in AS parameters between women with and without pregnancy complications. Since then, there has been a substantial influx of published research on the topic and a growing interest in the incorporation of AS measurements into clinical practice. Thus, we propose a systematic review and meta-analysis that is more inclusive to all HDP subsets and various hemodynamic indices of vascular health to provide a comprehensive overview of the current state of evidence. Specifically, we aim to evaluate these measures in women who develop HDPs compared to normotensive pregnancies to determine which measures are most associated with and/or can predict the development of HDPs. Major databases (Medline, Embase, The Cochrane Library, Web of Science, PubMed, and CINAHL), grey literature (Google Scholar) and clinical trials (clinicaltrials.gov) will be searched to identify studies that report AS and hemodynamic measurements in pregnant women with and without HDPs. No restrictions will be made on study type or year. Articles will be independently evaluated by three authors to determine eligibility based on inclusion and exclusion criteria. Methodological quality of included studies will be assessed. Pooled analyses will be conducted using a random-effects model. Publication bias and between-study heterogeneity will also be assessed. Sources of heterogeneity will be explored by sensitivity, subgroup, and/or meta-regression analyses. Results from this study will be shared through scientific conferences and publications in scientific journals. The analysis of potential AS and hemodynamic markers for HDP onset will help inform the development of screening guidelines and clinically relevant cut-off values of AS and hemodynamic markers for HDP risk, guiding future research. There are no applicable ethical considerations to the writing of this protocol.

An Early Screening Method for Dvt Based on the 3d- Reconstruction Using Mobile Phone Images

An Early Screening Method for Dvt Based on the 3d- Reconstruction Using Mobile Phone Images

Abstract PO-174: Using GIS mapping to explore factors in Texas' disparities in access to cancer care

Abstract Introduction: The significant decline in cancer incidence and mortality over the last two decades in the United States is likely the result of reductions in tobacco use, increased uptake of preventive methods, adoption of early screening methods and improved treatment options. Texas, due to its large size and significant rural and heterogeneous populations, continues to experience disparities in cancer incidence and access to care and clinical trials. This study aimed to: 1) assess cancer incidence in Texas for three target indications compared to national data, and 2) analyze potential disparities in access to care with respect to the locations of American College of Surgeons Accredited (ACoS) Cancer Centers and National Cancer Institute (NCI) Cancer Centers within Texas' 11 Public Health Regions (PHR). Methods: We used data from the Texas Cancer Registry and compared age-adjusted incidence for our three target indications (adult colon cancer in patients under 50 years of age, advanced cervical cancer, and advanced liver cancer) in the 11 PHR and the defined Border Region of Texas with national data from the US Surveillance, Epidemiology, and End Results Program (SEER). We used GIS mapping to locate the cancer centers in Texas' PHR. Results: Initial results indicate that all 11 Texas' PHR ranked higher than national averages for age-adjusted incidence of cervical and liver cancer. Further, incidence rates for adolescents and young adult colorectal cancer are above SEER average in the majority of the PHR located in the northern and eastern regions of the state. When compared with Texas incidence rates, PHR 1 (Lubbock) and PHR 11 (Harlingen) have higher incidence rates for all three indications. Two PHR in the Border region (PHR 10: El Paso and 11: Harlingen) have Texas' highest incidence of liver cancer. The distribution of ACoS centers appears concentrated in eastern Texas, with highest numbers in PHR 3 (Dallas area) and PHR 6 (Houston area); with three of the four NCI-designated cancer centers in Texas are located in these two regions. Lower rates of cancer incidence for all three cancer indications were found in PHR regions 3 and 6. Among the Border counties, Cameron, Maverick and Star counties ranked above the Texas' average for all three cancer indications. There is an apparent paucity of cancer centers in the Border Region with only three centers identified in the 32 counties within the region. Conclusion: Texas statewide ranked above SEER's averages in two of the three cancer indications-cervical and liver cancers. There are clear regional differences in incidence of cervical, liver, and colorectal cancer which suggest disparities in access to care. Future research will draw on these findings to assess distance to cancer centers and clinical trial locations for Texas patients in our three targeted cancers and determine if broadband coverage is associated with disparities in cancer rates. Citation Format: Yeka W. Nmadu, Deborah V. Dahlke, Scott A. Horel, Marcia G. Ory, Kenneth S. Ramos. Using GIS mapping to explore factors in Texas' disparities in access to cancer care [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-174.

Comparative Study for Prediction of Low and High Plasma Protein Binding Drugs by Various Machine Learning-Based Classification Algorithms

<p>In the drug discovery path, most drug candidates failed at the early stages due to their pharmacokinetic behavior in the system. Early prediction of pharmacokinetic properties and screening methods can reduce the time and investment for lead discoveries. Plasma protein binding is one of these properties which has a vital role in drug discovery and development. The focus of the current study is to develop a computational model for the classification of Low Plasma Protein Binding (LPPB) and High Plasma Protein Binding (HPPB) drugs using machine learning methods for early screening of molecules through WEKA. Plasma protein binding drugs data was collated from the Drug Bank database where 617 drug candidates were found to interact with plasma proteins, out of which an equal proportion of high and low plasma protein binding drugs were extracted to build a training set of ~300 drugs. The machine learning algorithms were trained with a training set and evaluated by a test set. We also compared various machine learning-based classification algorithms i.e., the Naïve Bayes algorithm, Instance-Based Learner (IBK), multilayer perceptron, and random forest to determine the best model based on accuracy. It was observed that the random forest algorithm-based model outperforms with an accuracy of 99.67% and 0.9933 kappa value on training set and on test set as compared to other classification methods and can predict drug plasma binding capacity in the given data set using the WEKA tool.</p>

Detecting early signs of Alzheimer’s disease and related dementia onset from the EHR

As dementia is widely under-detected, a predictive model using electronic health records (EHR) could provide a method for early screening to implement preventive strategies. There is limited research on using EHR to identify persons with Alzheimer’s disease (AD) and related dementias (RD). In a data-driven approach, we used all ICD-9 diagnosis and CPT procedure codes from statewide inpatient, ambulatory surgery, and Medicare records, in addition to age at baseline and gender, to detect AD/RD from the Cache County Study on Memory in Aging (1995–2009). After removing participants diagnosed with dementia at baseline (n=335), 3882 (82%) Cache County Study participants could be linked to inpatient, ambulatory surgery, and/or Medicare EHR records; 484 (12.5%) of these 3882 had incident all-cause dementia, with 308 (7.9%) having AD/AD comorbid with RD; and 176 (4.5%) having RD without AD. We removed participant’s ICD-9 codes occurring after first AD/RD diagnoses. EHR features (~2000) along with gold-standard diagnoses as class labels were then used to train and detect AD and/or RD using a Gradient Boosting Trees machine learning algorithm. Models evaluated with nested cross-validation yielded AUCs of 0.70 for all-cause dementia, 0.69 for AD/AD comorbid with RD, and 0.67 for RD without AD. Key factors detecting AD/RD included age at enrollment, cardiovascular, metabolic, and kidney disease, and sleep disturbances, with feature importance varying by record type and time frame prior to dementia onset. Our findings suggest that a patient’s health status up to 12 years prior may be useful in identifying individuals at-risk for dementia development.

Serum Raman spectroscopy combined with convolutional neural network for label‐free detection of echinococcosis

AbstractEarly disease screening is of great significance to its prevention and treatment, and serum analysis is one of the important methods for early disease screening. In this paper, a portable spectrometer was used to collect serum Raman spectra of echinococcosis patients and healthy volunteers, and a one‐dimensional convolutional neural network (CNN) was used to classify the two types of spectra. The average spectrum shows that the phenylalanine and carotene content in the serum of patients with echinococcosis is less than that in healthy volunteers, whereas the content of amino acids such as proline and tyrosine is higher. We built a CNN model to identify two types of spectra. First, the Kennard–Stone algorithm was used to divide the two types of spectra into a training set and a test set. Afterwards, the optimal hyperparameters of the model were determined by fivefold cross‐validation on the training set. Finally, the test set was used to evaluate the final effect of the model. The accuracy, sensitivity, and specificity of the test set on the model are 94.90%, 90.57%, and 97.11%, respectively. The receiver operating characteristic curve analysis further confirmed the excellent performance of the CNN in the classification of serum Raman spectra. The above results show that serum Raman spectroscopy combined with a one‐dimensional CNN algorithm has great potential in the early diagnosis of echinococcosis.

CLOUD-BASED TEMPERATURE MONITORING MECHANISM FOR COVID-19 PANDEMIC TRACKING

The unprecedented outbreak of novel coronavirus 2019 (COVID-19) globally has a huge impact to our daily life in numerous ways. To effectively minimize the spread of the virus, early symptom detection is crucial, especially in closed environment with high human traffic areas which post higher chances of human-to-human transmission. Body temperature measurement has been identified among the vital monitoring parameters. However, current available temperature monitoring mechanism is costly, limited to single individual and limited to locally without integrating to cloud and database. This led to difficulty in effective surveillance for suspicious COVID cases. Hence, the purpose of this paper is to introduce an end-to-end Internet of Things-enabled application for thermal monitoring as an early signal detection and screening method. This work integrates Raspberry Pi, thermal sensor, LCD display, buzzer, and LED light with Raspbian and Restful API for device-to-cloud communication. The system implemented is capable for user identification, body temperature remote monitoring and warning signal for fever symptoms. The result of this real-time system is capable to detect and screen the suspected contagious person in an organization effectively. Future works on integrating face recognition with machine learning and artificial intelligent enhancement.

The Earliest Stage of Lung Adenocarcinoma: the Pathological Diagnosis and Clinical Significance of Adenocarcinoma In Situ

第五版《世界卫生组织（World Health Organization, WHO）胸部肿瘤分类》（2021版分类）已于2021年5月正式出版，距离上一版2015版WHO分类不过只有短短6年时间。随着近年来低剂量螺旋计算机断层扫描（computed tomography, CT）作为早期肺部肿瘤筛查手段的应用，肺腺癌早已成为许多医院手术治疗的优势病种。WHO分类作为指导病理诊断的权威指南，任何一处细微的变动都牵扯病理、临床及患者的心弦。原位腺癌为2015版分类新增加的腺癌诊断，也是近年来国内外临床及研究的焦点，因新版分类对其目录位置进行了一定调整，由此在临床医师及患者中产生了“原位腺癌被剔除出恶性肿瘤范畴”的巨大争议和讨论，本文将就肺原位腺癌诊断的来龙去脉，新分类目录调整以及原位腺癌良恶性的问题进行简要说明。

In Vivo Imaging-Based Techniques for Early Diagnosis of Oral Potentially Malignant Disorders-Systematic Review and Meta-Analysis.

Objectives: Oral potentially malignant disorders (OPMDs) are lesions that may undergo malignant transformation to oral cancer. The early diagnosis and surveillance of OPMDs reduce the morbidity and mortality of patients. Diagnostic techniques based on medical images analysis have been developed to diagnose clinical conditions. This systematic review and meta-analysis aimed to evaluate the efficacy of imaging-based techniques compared to the gold standard of histopathology to assess their ability to correctly identify the presence of OPMDs. Design: Literature searches of free text and MeSH terms were performed using MedLine (PubMed), Scopus, Google Scholar, and the Cochrane Library (from 2000 to 30 June 2020). The keywords used in the search strategy were: (“oral screening devices” or “autofluorescence” or “chemiluminescence” or “optical imaging” or “imaging technique”) and (“oral dysplasia” or “oral malignant lesions” or “oral precancerosis”). Results: The search strategy identified 1282 potential articles. After analyzing the results and applying the eligibility criteria, the remaining 43 papers were included in the qualitative synthesis, and 34 of these were included in the meta-analysis. Conclusions: None of the analyzed techniques based on assessing oral images can replace the biopsy. Further studies are needed to explore the role of techniques-based imaging analysis to identify an early noninvasive screening method.

A Novel and Rapid Serum Detection Technology for Non-Invasive Screening of Gastric Cancer Based on Raman Spectroscopy Combined With Different Machine Learning Methods.

Gastric cancer (GC) is the fifth most common cancer in the world and a serious threat to human health. Due to its high morbidity and mortality, a simple, rapid and accurate early screening method for GC is urgently needed. In this study, the potential of Raman spectroscopy combined with different machine learning methods was explored to distinguish serum samples from GC patients and healthy controls. Serum Raman spectra were collected from 109 patients with GC (including 35 in stage I, 14 in stage II, 35 in stage III, and 25 in stage IV) and 104 healthy volunteers matched for age, presenting for a routine physical examination. We analyzed the difference in serum metabolism between GC patients and healthy people through a comparative study of the average Raman spectra of the two groups. Four machine learning methods, one-dimensional convolutional neural network, random forest, support vector machine, and K-nearest neighbor were used to explore identifying two sets of Raman spectral data. The classification model was established by using 70% of the data as a training set and 30% as a test set. Using unseen data to test the model, the RF model yielded an accuracy of 92.8%, and the sensitivity and specificity were 94.7% and 90.8%. The performance of the RF model was further confirmed by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.9199. This exploratory work shows that serum Raman spectroscopy combined with RF has great potential in the machine-assisted classification of GC, and is expected to provide a non-destructive and convenient technology for the screening of GC patients.

Outcomes of pregnancies with trisomy 16 mosaicism detected by NIPT: a series of case reports

BackgroundTrisomy 16 (T16) is thought to be the most frequent chromosome abnormality at conception, which is often associated with a high risk of abnormal outcomes.MethodsA retrospective analysis of 14 cases with high risk of T16 by noninvasive prenatal testing (NIPT) was conducted. All cases in the analysis involved prenatal diagnosis, karyotyping and chromosomal microarray analysis.Case reportsNIPT detected 12 cases of T16 and 2 cases of T16 mosaicism. Prenatal diagnosis confirmed 5 true positive cases and 9 false positive cases. Among the 5 true positive cases, 3 cases had ultrasound abnormalities. All of the 9 false positive cases continued their pregnancies. The newborns who were from these 9 false positive cases except 1 case (case 7) had low birth weights (< 2.5 kg) and there were also 2 premature deliveries.ConclusionNIPT serves as a fast and early prenatal screening method, giving clues to chromosome abnormalities and providing guidance for managing pregnancy. Confined placental mosaicism in 16 pregnancies may be at higher risk for preterm delivery.

Correlation of yield with early seedling performance and physio-biochemical traits in Basmati rice mutants subjected to heat stress

The present study aims at deciphering the response of Basmati rice mutants to high temperature stress. The work aims to find an early screening method by correlating seedling and physiological response to yields. After rigorous screening in controlled environment (growth chamber) and field conditions over a period of four years 16 mutants’ lines were selected: HTT-18, HTT-29, HTT-31, HTT-39, HTT-51, HTT-53, HTT-74, HTT-81, HTT-92, HTT-97, HTT-98, HTT-104, HTT-114, HTT-119, HTT-132 and HTT-138. These have been tested against standards: Super Basmati and IR-64. Field trials were conducted at three locations while early seedling-stage traits and their biochemical analyses were studied in growth chamber experiments. Data of seedling traits were used to establish correlations with paddy yield under hot field conditions. The temperatures were high at two sites: Multan and Bahawalpur ranging 24-46 °C and 25-45 °C respectively), and relatively lower at NIAB field station (26-45 °C. The paddy yield was significantly correlated with early seedling-stage traits such as shoot length (0.79**), shoot fresh and dry weight (0.48* and 0.49*), and cell membrane thermo-stability (0.60**). Additionally, significant higher activities of antioxidants (SOD and APX) and lower stress indicators (MDA, esterase and TOS) were observed in the heat tolerant mutants. These mutants were classified for their heat tolerance by principle component analysis (PCA) using yield-correlated early seedling-stage and other physio-biochemical parameters. As a result, the heat tolerance classification of mutants based on PCA coincided with the yield of mutants grown under hot field conditions. The present study suggests that these seedling parameters may be used as surrogates for field performance and used in the selection of thermo-tolerant Basmati rice

Mass spectrometry based proteome profiling of the exhaled breath condensate for lung cancer biomarkers search

ABSTRACT Introduction Lung cancer remains the most prevalent cause of cancer mortality worldwide mainly due to insufficient availability of early screening methods for wide-scale application. Exhaled breath condensate (EBC) is currently considered as one of the promising targets for early screening and is particularly attractive due to its absolutely noninvasive collection and possibility for long-term frozen storage. EBC proteome analysis can provide valuable information about the (patho)physiological changes in the respiratory system and may help to identify in time a high risk of lung cancer. Mass spectrometry (MS) profiling of EBC proteome seems to have no alternative in obtaining the most extensive data and characteristic marker panels for screening. Areas covered This special report summarizes the data of several proteomic studies of EBC in normal and lung cancer (from 2012 to 2021, PubMed), focuses on the possible reasons for the significant discrepancy in the results, and discusses some aspects for special attention in further studies. Expert opinion The significant discrepancy in the results of various studies primarily highlights the need to create standardized protocols for the collection and preparation of EBC for proteomic analysis. The application of quantitative and targeted LC-MS/MS based approaches seems to be the most promising in further EBC proteomic studies.

Early Diagnosis of Occult Blood of Colorectal Cancer Based on Nano-Colloidal Gold Sandwich Immunochromatography.

The development of science and technology has deepened people's understanding of cancer, changing the management of malignant tumors in the medical field. Given the common precancerous characteristics of colorectal cancer (CRC), researchers studied early CRC screening. The complexity of traditional diagnostics forced medical staff to speed up CRC innovation early screening methods. Here, we prepared nano-colloidal gold raw materials with different particle sizes (15 and 30 nm) and observed the morphological characteristics and properties of the materials. Simultaneously, the nanocolloidal gold double antibody sandwich kit was designed through the optimum pH value and protein content screening experiment. The results of clinical enteroscopy confirmed the important guiding significance of the equipment in early CRC screening.

Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review.

Pancreatic cancer is a highly malignant digestive system tumor with a poor prognosis. Most pancreatic cancer patients are diagnosed at an advanced stage or even metastasis due to its highly aggressive characteristics and lack of typical early symptoms. Thus, an early diagnosis of pancreatic cancer is crucial for improving its prognosis. Currently, screening is often applied in high‐risk individuals to achieve the early diagnosis of pancreatic cancer. Fully understanding the risk factors of pancreatic cancer and pathogenesis could help us identify the high‐risk population and achieve early diagnosis and timely treatment of pancreatic cancer. Notably, accumulating studies have been undertaken to improve the detection rate of different imaging methods and the diagnostic accuracy of endoscopic ultrasound‐guided fine‐needle aspiration (EUS‐FNA) which is the golden standard for pancreatic cancer diagnosis. In addition, there are currently no biomarkers with sufficient sensitivity and specificity for the diagnosis of pancreatic cancer to be applied in the clinic. As the only serum biomarker approved by the United States Food and Drug Administration, carbohydrate antigen 19‐9 (CA19‐9) is not recommended to be used in the early screening of pancreatic cancer because of its limited specificity. Recently, increasing numbers of studies focused on the discovering of novel serum biomarkers and exploring their combination with CA19‐9 in the detection of pancreatic cancer. Besides, the application of liquid biopsy involving circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNAs (miRNAs), and exosomes in blood and biomarkers in urine, and saliva in pancreatic cancer diagnosis are drawing more and more attention. Furthermore, many innovative technologies such as artificial intelligence, computer‐aided diagnosis system, metabolomics technology, ion mobility spectrometry (IMS) associated technologies, and novel nanomaterials have been tested for the early diagnosis of pancreatic cancer and have shown promising prospects. Hence, this review aims to summarize the recent progress in the development of early screening and diagnostic methods, including imaging, pathological examination, serological examination, liquid biopsy, as well as other potential diagnostic strategies for pancreatic cancer.

A quantum-clustering optimization method for COVID-19 CT scan image segmentation

The World Health Organization (WHO) has declared Coronavirus Disease 2019 (COVID-19) as one of the highly contagious diseases and considered this epidemic as a global health emergency. Therefore, medical professionals urgently need an early diagnosis method for this new type of disease as soon as possible. In this research work, a new early screening method for the investigation of COVID-19 pneumonia using chest CT scan images has been introduced. For this purpose, a new image segmentation method based on K-means clustering algorithm (KMC) and novel fast forward quantum optimization algorithm (FFQOA) is proposed. The proposed method, called FFQOAK (FFQOA+KMC), initiates by clustering gray level values with the KMC algorithm and generating an optimal segmented image with the FFQOA. The main objective of the proposed FFQOAK is to segment the chest CT scan images so that infected regions can be accurately detected. The proposed method is verified and validated with different chest CT scan images of COVID-19 patients. The segmented images obtained using FFQOAK method are compared with various benchmark image segmentation methods. The proposed method achieves mean squared error, peak signal-to-noise ratio, Jaccard similarity coefficient and correlation coefficient of 712.30, 19.61, 0.90 and 0.91 in case of four experimental sets, namely Experimental_Set_1, Experimental_Set_2, Experimental_Set_3 and Experimental_Set_4, respectively. These four performance evaluation metrics show the effectiveness of FFQOAK method over these existing methods.

ICU Acquired Weakness in patients with respiratory failure

Background:Most patients with severe respiratory failure in intensive care unit (ICU) require bed rest. The limitation of physical activity leads to some adverse consequences such as ICU Acquired Weakness (ICUAW). Progression of respiratory failure, including that caused by the new coronavirus infection (COVID-19), can lead to the development of acute respiratory distress syndrome, the treatment of which contributes to a combination of risk factors for the development of ICUAW. Traditional diagnostic methods have certain limitations. Muscle ultrasonography is a modern tool for early detection of muscle mass loss.Aims:To compare different methods of early ICUAW screening and to estimate the incidence and peculiarities of ICUAW in patients with respiratory failure of infectious genesis.Methods:31 patients with severe coronavirus pneumonia (COVID-19+) and 13 patients with viral and/or bacterial lung infection (COVID-19 -) were included in the study. The muscle mass loss percent from day 1 to day 7 was higher in the COVID-19 - group (p=0.022). These patients also had longer durations of the ICU and hospital stay but a significantly lower mortality (2.5 times).Results:The analysis of the parameters of deceased and living patients regardless of the lung damage etiology showed a correlation between the indices of hand grip strength dynamometry (handgrip test) and ultrasonography of the thigh muscles: F1 and D1 (rho=0.6, p=0.003), F1 and S1 (rho=0.6, p=0.005), D1 and F7 (rho=0.9, p=0.001). In addition, the examined levels of the ICUAW markers were associated with age - F1 (rho=-0.6, p=0.001), D1 (rho=-0.4, p=0.003), S1 (rho=-0.4, p=0.004).Conclusions:During the critical illness, ICUAW develops by the 3d day of bed rest in two thirds of patients with respiratory failure of different infectious genesis. The correlation between the investigated markers of ICUAW and age indicates that elderly patients are the most vulnerable category in respect to the formation and progression of muscle weakness in the ICU. The handgrip test can serve as a reliable and simple method of ICUAW screening. Early identification of patients with ICUAW should provide the improvement of nutritional support and individualization of rehabilitation.