Low Detection Rate Research Articles

Innovative Solutions for Multidrug-resistant Organisms Infections in ICU: A Joint Efficacy Evaluation of Multi-disciplinary Team and SHEL Model (Software, Hardware, Environment, Liveware)

BackgroundThe escalating threat of Multidrug-resistant organisms (MDROs) in Intensive Care Units (ICU) demands innovative management strategies to curb the rising infection rates and associated clinical challenges. ObjectiveTo assess the effectiveness of integrating the Multi-disciplinary team (MDT) approach with the SHEL model (Software, Hardware, Environment, Liveware) in reducing MDROs infections within ICU settings. MethodsFrom January 2021 to April 2024, a prospective, randomized controlled study was conducted in the ICU of Nantong Fourth People's Hospital, enrolling 411 patients with MDROs infections. These patients were randomly assigned into three groups: the MDT group, the SHEL model group, and a combined group. The intervention lasted for four weeks, during which the effects on the MDROs detection rate, infection rate, healthcare staff's infection control execution scores, and the rationality of antibiotic use were assessed, aiming to determine the efficacy of each approach in managing MDROs in the ICU setting. ResultsThe overall infection rate of MDROs in the ICU of our hospital from 2021 to 2024 was 60.18%, with sputum infection sources accounting for 68.37% of the total sources, making it the primary source of infection. The detection rate of MOROs in the combined group was significantly higher than that in the MDT group and the SHEL group, with the SHEL group having a higher detection rate than the MDT group (P<0.05). The infection rate of MOROs in the combined group was significantly lower than that in both the MDT group and the SHEL group, with the SHEL group having a lower detection rate than the MDT group (P<0.05). The implementation scores of the combination group in standard prevention, hand hygiene, antibiotic management, and isolation measures were significantly higher than those of the MDT and SHEL groups, with the SHEL group scoring higher than the MDT group (P<0.05). The rational use of antibiotics in the combined group was also higher than in both the MDT group and the SHEL group, with the SHEL group having a higher level than the MDT group (P<0.05). ConclusionThe integrated MDT and SHEL model significantly reduced MDROs infections in ICU, improved healthcare workers' infection prevention and nursing quality, and promoted the appropriate use of antibiotics, advocating for its clinical application.

A Prioritization Method for Combinatorial Test Cases Based on Convolutional Neural Network with Wide Multi-Layer Kernels and Its Application

When the quantity of parameters or values in systems being tested is substantial, there is a significant escalation in the number of combinatorial test cases. The execution of all test cases will require a substantial allocation of time and resources. Prioritization technology enables early detection of system faults and enhances test efficiency. Most existing prioritization methods rely on historical empirical data, which can be challenging to obtain in many cases. In the meantime, random prioritization often leads to lower fault detection rates. This paper presents a prioritization method for combinatorial test cases based on a convolutional neural network (CNN) model. The weights of suspected fault-inducing interactions are initially extracted through a convolution operation in the subset of upfront test cases. Second, the features related to fault-inducing interactions are derived using wide multi-layer kernels convolutional neural network (WCNN). Third, the deep WCNN-SVM model undergoes training and makes predictions on the entire set of test cases. The predicted results are then combined with weights to prioritize combinatorial test cases. Test cases of equal priority are adjusted based on distance entropy. Application experiments on UAV demonstrate that the proposed method effectively enhances both fault detection speed and fault detection rate.

CFD Analysis of the Choledynamic Flow Characteristics of a Patient with Gallbladder Carcinoma

Gallbladder carcinoma (GBC) is a rare malignancy with a low detection rate due to non-specific symptoms, complicating early diagnosis and treatment. This study aims to improve pre-operative and post-operative surgical analysis using patient-specific CFD analysis. CT scan images of a male patient with GBC were segmented using 3D Slicer software, and the biliary tree geometry was imported into ANSYS Workbench for fluid and solid meshing. The mechanical properties of the biliary tree and the rheological properties of bile were determined, with an LRN k-ω model used for simulating the gallbladder refilling stage. Results indicate that unhealthy bile flows slower than healthy bile, suggesting a link between obstructed bile flow and tumors or stones. Tumor-induced blockages cause pressure accumulation in the gallbladder neck, unlike the minimum pressure in healthy cases. These findings highlight the complex interplay between bile rheology, tumor development, and pressure dynamics, suggesting the model’s potential use in surgical planning.

Perinatal outcomes after selective third‐trimester ultrasound screening for small‐for‐gestational age: prospective cohort study nested within DESiGN randomized controlled trial

ABSTRACTObjectiveIn screening for small‐for‐gestational age (SGA) using third‐trimester antenatal ultrasound, there are concerns about the low detection rates and potential for harm caused by both false‐negative and false‐positive screening results. Using a selective third‐trimester ultrasound screening program, this study aimed to investigate the incidence of adverse perinatal outcomes among cases with (i) false‐negative compared with true‐positive SGA diagnosis and (ii) false‐positive compared with true‐negative SGA diagnosis.MethodsThis prospective cohort study was nested within the UK‐based DESiGN trial, a prospective multicenter cohort study of singleton pregnancies without antenatally detected fetal anomalies, born at &gt; 24 + 0 to &lt; 43 + 0 weeks' gestation. We included women recruited to the baseline period, or control arm, of the trial who were not exposed to the Growth Assessment Protocol (GAP) intervention and whose birth outcomes were known. Stillbirth and major neonatal morbidity were the two primary outcomes. Minor neonatal morbidity was considered a secondary outcome. Suspected SGA was defined as an estimated fetal weight (EFW) &lt; 10th percentile, based on the Hadlock formula and fetal growth charts. Similarly, SGA at birth was defined as birth weight (BW) &lt; 10th percentile, based on UK population references. Maternal and pregnancy characteristics and perinatal outcomes were reported according to whether SGA was suspected antenatally or not. Unadjusted and adjusted logistic regression models were used to quantify the differences in adverse perinatal outcomes between the screening results (false negative vs true positive and false positive vs true negative).ResultsIn total, 165 321 pregnancies were included in the analysis. Fetuses with a false‐negative SGA screening result, compared to those with a true‐positive result, were at a significantly higher risk of stillbirth (adjusted OR (aOR), 1.18 (95% CI, 1.07–1.31)), but at lower risk of major (aOR, 0.87 (95% CI, 0.83–0.91)) and minor (aOR, 0.56, (95% CI, 0.54–0.59)) neonatal morbidity. Compared with a true‐negative screening result, a false‐positive result was associated with a lower BW percentile (median, 18.1 (interquartile range (IQR), 13.3–26.9)) vs 49.9 (IQR, 30.3–71.7)). A false‐positive result was also associated with a significantly increased risk of stillbirth (aOR, 2.24 (95% CI, 1.88–2.68)) and minor neonatal morbidity (aOR, 1.60 (95% CI, 1.51–1.71)), but not major neonatal morbidity (aOR, 1.04 (95% CI, 0.98–1.09)).ConclusionsIn selective third‐trimester ultrasound screening for SGA, both false‐negative and false‐positive results were associated with a significantly higher risk of stillbirth, when compared with true‐positive and true‐negative results, respectively. Improved SGA detection is needed to address false‐negative results. It should be acknowledged that cases with a false‐positive SGA screening result also constitute a high‐risk population of small fetuses that warrant surveillance and timely birth. © 2024 The Author(s). Ultrasound in Obstetrics &amp; Gynecology published by John Wiley &amp; Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Improved identification and tracking of three-dimensional eddies in the Southern Ocean utilizing 3D-U-Res-Net

Oceanic mesoscale eddies are prevalent throughout the global ocean, playing a critical role in material and energy transport while significantly influencing climate change. Accurate characterization of their three-dimensional structures and movement is essential for a quantitative analysis of their transport processes. Traditional eddy detection algorithms have lower successful detection rate and with more limitations, so they fall short in the complex and dynamic ocean environment. The rising trend of applying artificial intelligence (AI) algorithms, due to their efficiency, precision, and automation, addresses this challenge. This study employs the 3D-U-Res-Net algorithm to identify the three-dimensional structures of mesoscale eddies in the Southern Ocean using GLORYS12V1 data from 2011 to 2020. A vector geometry-based eddy detection algorithm (VG) initially identified 1587292 eddy snapshots in the Southern Ocean (2011–2019), which were used for training the 3D-U-Res-Net algorithm. Data from 2020 served as the ground truth and validation set. The successful detection rate of 3D-U-Res-Net algorithm is 100%, which means that it identified all 135734 eddy snapshots from the VG dataset in 2020. For eddy tracking, the VG algorithm counted 18168 eddy tracks, whereas the 3D-U-Res-Net counted 18559, reflecting a 2.15% bias. To reduce uncertainty, eddies with lifespans shorter than two weeks were excluded. The average lifespans and traveling distances for eddies detected by the 3D-U-Res-Net (VG) algorithm were 29.35 (29.61) days and 77.78 (37.60) km, respectively, with the 3D-U-Res-Net identifying eddies with longer traveling distances. The mean radius of eddies detected by the VG algorithm was 43.16 km, while the 3D-U-Res-Net detected eddies with a mean radius of 43.74 km, a 0.58 km increase. We categorized eddies into four three-dimensional structures: bowl-shaped, cone-shaped, lens-shaped, and cylindrical. The VG algorithm identified these structures in proportions of 32%, 31%, 25%, and 12%, respectively, whereas the 3D-U-Res-Net algorithm found 19.48%, 19.58%, 0.04%, and 60.9%, respectively. The 3D-U-Res-Net identified more cylindrical eddies and was approximately ten times faster than the VG algorithm. Overall, this algorithm has good performance and higher efficiency. It is an attempt of using AI for oceanic research, and more works can be carried out in the future.

Benchmarking three DNA metabarcoding technologies for efficient detection of non-native cerambycid beetles in trapping collections

Individual sorting and identification of thousands of insects collected in mass trapping biosurveillance programmes is a labour-intensive and time-consuming process. Metabarcoding allows the simultaneous identification of multiple individuals in a single mixed sample and has the potential to expedite this process. However, detecting all the species present in a bulk sample can be challenging, especially when under-represented non-native specimens are intercepted. In this study, we quantified the effectiveness of DNA metabarcoding at detecting exotic species within six different mock communities of native and non-native species of European xylophagous cerambycid beetles. The main objective is to compare three different sequencing technologies (MinION, Illumina and IonTorrent) to evaluate which one is the most suitable in this context. Additionally, dry and wet (monopropylene glycol and water) collection methods were compared. Although not observing significant differences in the total number of species detected amongst the three sequencing technologies, the MinION detected a greater number of species in field-like samples. All three sequencing technologies achieved success in detecting and identifying closely-related species and species in low abundance. The capture method of insects in the field greatly influenced sample preservation and detection. Individuals captured in traps containing monopropylene and water had lower DNA concentration, leading to lower species detection rates compared to individuals killed using just an insecticide without any collection medium.

Highly sensitive deep panel sequencing of 27 HPV genotypes in prostate cancer biopsies results in very low detection rates and indicates that HPV is not a major etiological driver of this malignancy

BackgroundHuman papillomavirus (HPV) has been proposed to contribute to the carcinogenesis of prostate cancer. However, previous studies have yielded conflicting results. This study aims to add useful information to the ongoing discussion concerning the association between HPV infection and prostate cancer.MethodsWe used two high-throughput next-generation sequencing (NGS) approaches to detect HPV RNA in malignant and adjacent normal (AN) prostate tissue (cohorts 1 and 2) and HPV DNA from carcinogenic and probably/possibly carcinogenic-classified HPV types (cohort 3) in malignant prostate, AN prostate, and benign prostatic hyperplasia (BPH) tissues.ResultsIn total, 0% (cohort 1: 0/83, cohort 2: 0/16) of the malignant prostate tissue samples and 0% (cohort 1: 0/23, cohort 2: 0/8) of the AN prostate tissue samples were positive for HPV RNA. A total of 8.3% (1/12) of the BPH samples, 0% (0/28) of the AN samples, and 0.8% (1/132) of the malignant prostate samples were positive for HPV16 DNA. However, the normalized read count of the HPV16-positive malignant sample was close to the cut-off. In addition, no other carcinogenic-classified HPV types were detected in any of the BPH, AN, or malignant prostate tissue samples.ConclusionOur study does not support HPV infection as a major contributor to the etiology of prostate cancer.

Abstract A004: Full spectrum isolation technique-based dual circulating rare cells analysis to maximize early cancer diagnosis

Abstract Liquid biopsy (LB) is essential in precision medicine, enabling real-time tumor monitoring through repeated sampling. However, its diagnostic sensitivity and accuracy are limited by the low detection rate of circulating tumor cells (CTCs). For example, in breast cancer, the CTC detection rate exhibited only 20%, significantly limiting its clinical utility. This limitation arises since existing technologies selectively isolate specific phenotypes of CTCs, failing to capture others, such as mesenchymal CTCs, which are associated with poor prognosis. These mesenchymal CTCs often evade detection due to their lack of EpCAM expression and high deformability, making difficult to isolate them with traditional filter or EpCAM based methods. Additionally, conventional LB methods fail to provide critical insights into the tumor microenvironment (TME). To overcome these limitations, we developed a novel approach that improves LB diagnostic sensitivity and accuracy by (1) creating a technology to isolate all types of CTCs, regardless of marker expression or size, and (2) simultaneously analyzing CTCs and circulating cancer-associated fibroblasts (cCAFs). This dual approach addresses the challenges of low sensitivity and accuracy by enabling the isolation of a broader range of CTCs, including mesenchymal types, and by incorporating cCAF analysis. cCAFs, derived from the TME and found in the bloodstream, provide valuable insights into the TME that are hard to obtain through traditional methods. Analyzing cCAFs alongside CTCs offers a more comprehensive understanding of tumor biology and its microenvironment, leading to improved diagnostic accuracy, especially in early-stage cancer. In our study involving 55 breast cancer patients, we demonstrated that while CTCs were undetectable in 42.9% of early-stage patients, cCAFs were detected in all cases and were found to be 10 times more abundant than CTCs. Importantly, for the first time, this study uncovers that the diverse CAF subtypes previously identified in tissue samples also exist in the bloodstream, highlighting their critical role in providing TME-related insights. In addition, cCAF subtypes vary with hormonal receptor expression, and their marker analysis allows for 80.9% accurate classification of breast cancer subtypes. We applied this approach to early-stage breast cancer patients, for whom diagnosis through liquid biopsy is particularly difficult, and found that the combined analysis of CTCs and cCAFs achieved a 18% improvement in accuracy, with the AUC increasing from 0.6817 to 0.8009 compared to CTC analysis alone. This innovative approach significantly enhances LB by simultaneously isolating all CTC subtypes and incorporating cCAF analysis, thereby offering a more sensitive and accurate diagnostic tool. Beyond improving breast cancer diagnostics, this method has the potential to be applied to other tumor types and could also prove valuable for prognosis prediction and therapeutic response monitoring, ultimately leading to better patient outcomes and more effective cancer treatment strategies. Citation Format: Hyeongjung Woo, Simon A Joosse, Frederike Harten, Felix Hilpert, Klaus Pantel, Minseok S. Kim. Full spectrum isolation technique-based dual circulating rare cells analysis to maximize early cancer diagnosis [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A004.

Autonomous inspection robot for dead laying hens in caged layer house

Daily inspections of individual laying hens in large-scale egg farms are both labor-intensive and time-consuming, requiring farm staff to manually check each caged hen and promptly remove any deceased birds to prevent the spread of disease within the battery cages. To streamline this process, a specialized robot has been developed to enhance inspection efficiency, reduce manual labor, and enable rapid identification of dead hens. This inspection robot integrates cutting-edge technologies such as deep learning for real-time detection and identification, QR code-based positioning for precise localization, and autonomous navigation for seamless movement through the farm. It automates the otherwise tedious inspection process by visualizing and pinpointing the location of dead hens within the cages. In experimental tests, the robot achieved a detection accuracy of 90.61 % by incorporating a supplementary lighting system, setting an inspection speed of 9 m per minute, and fine-tuning the inspection algorithm with a probability value parameter of 0.48 and an area ratio parameter of 0.05. Additionally, the robot demonstrated a low false detection rate of 0.14 % and a minimal obvious false detection rate of 0.06 %. Compared to traditional manual inspection methods, this robotic system not only automates the task but also significantly reduces labor requirements and improves the overall management efficiency of large-scale egg farms. With its high accuracy and speed, the robot presents a viable solution for modern poultry operations, ensuring timely removal of dead hens and contributing to better farm hygiene and animal welfare.

Retrospective study on the strength of magnetic resonance signs for predicting breast implant rupture: assessing the impact of radiologist expertise at a breast cancer referral center.

This study evaluates the diagnostic criteria of MRI for breast implant rupture and examines the influence of radiologist experience on the accuracy of rupture detection. A retrospective study was conducted in a single center, involving patients who underwent implant replacement surgery between March 2019 and October 2022. MRI evaluations by four radiologists of varying experience levels were compared with surgical outcomes. The study included 118 explanted prostheses, with 25 identified as ruptured. Expert radiologists demonstrated near-perfect agreement (κ = 0.94) in identifying rupture signs, with high PPV and NPV, whereas non-experts showed only moderate agreement (κ = 0.44). Indicators such as the linguine sign, subcapsular line, and keyhole sign were identified with high accuracy by experts, contrasting with non-experts' lower detection rates. Expertise in radiology significantly impacts the accurate diagnosis of breast implant ruptures. This study supports updating radiological guidelines and underscores the importance of specialized training and experience in improving diagnostic outcomes in breast implant assessments.

An algorithm for lane detection based on RIME optimization and optimal threshold

In order to address the challenges of low lane line detection rates caused by complex road conditions,we propose a novel algorithm that integrates frost and ice optimisation with optimal thresholding. A pre-processing model based on Retinex theory is used to reduce noise and preserve grey scale detail. The optimal OTSU threshold is determined for segmentation, which is enhanced by tent mapping. To further enhance the precision of the detection process, the binarized image is transformed into a bird’s-eye view, and the lane line pixel features are identified through the use of an adaptive sliding window. Ultimately, the RANSAC algorithm is utilized in conjunction with a parabolic model for lane line fitting. The experimental results demonstrate that, in comparison to similar image segmentation algorithms, the proposed method exhibits a notable advantage in terms of threshold calculation error and computational efficiency. Moreover, in comparison to analogous line detection algorithms, the detection accuracy rate reaches 93.87%, effectively reducing the impact of interference factors and demonstrating remarkable robustness that surpasses the traditional Hough Transform, which has an accuracy of 43.2%, and sliding window and Hough transform, with an accuracy of 89.16%. The code of our research work is publicly available at: https://github.com/zx2000430/rime.

Racial Disparities in Genetic Detection Rates for Inherited Retinal Diseases

The association of race and detection of pathogenic variants using wide-panel genetic testing for inherited retinal diseases (IRD), to our knowledge, has not been studied previously. To investigate the genetic detection rates of wide-panel testing in Black and non-Hispanic White patients with IRDs. This 2-group comparison used retrospective patient data that were collected at the University of Michigan (UM) and Blueprint Genetics (BG). At UM, inclusion criteria included having a clinical IRD diagnosis, wide-panel genetic testing, and both parents and the patient self-identifying as the same race (Black or non-Hispanic White). Logistic regression analysis was used; the dependent variable was genetic test result (positive or negative/inconclusive) and the independent variables were race, age, sex, phenotype, and number of genes tested. In the BG database, patients with wide-panel testing and self-reported race were included; detection rate comparison analysis based on race was performed using χ2 test of independence. These data were analyzed from October 30, 2013, through October 26, 2022. Genetic test result was considered positive if pathogenic/likely pathogenic variants were detected. A total of 572 patients were included in UM, 295 were males (51.6%). Mean age was 45 years. There were 54 Black patients (9.4%) and 518 White patients (90.6%). Black race (odds ratio [OR], 0.25; 95% CI, 0.14-0.46; P < .001) and age (OR per 10 years, 0.84; 95% CI, 0.76-0.92; P < .001) were independently associated with decreased odds of a positive test. In the BG database, 142 of 320 of Black patients (44.4%) had a positive/likely positive test result, a proportion lower than White patients (1691 of 2931 [57.7%]) (χ2 = 18.65; df = 1; P < .001). Results from this study highlight a lower genetic detection rate for Black patients than for White patients with IRDs. This supports a concern that the current development of IRD therapeutics is highly dependent on the ability to identify the genetic cause of disease. Patients with no known genetic diagnosis may be disadvantaged in terms of prognostication, inheritance counseling, reproductive decision-making, and eligibility for potential therapeutic options, including clinical trials. As future treatments become available, these findings suggest the need to examine the genetic detection rates across majority and minority subgroups alike.

Evaluating catatonia care in the psychiatric emergency room: A retrospective study

BackgroundCatatonia, characterized by motor, affective, and speech disturbances, is a prevalent yet frequently misdiagnosed syndrome in the psychiatric domain, with reported prevalence ranging from 5% to 18%. This study aimed to ascertain the incidence of catatonia diagnoses and the quality of care provided within the psychiatric emergency room (ER), while also evaluating psychiatrists' general awareness of the syndrome through a survey. MethodsA retrospective analysis of psychiatric ER records spanning June 18th to August 20th, 2022, was conducted to assess catatonia diagnosis frequency and treatment approaches. A Google Survey was distributed to full-time psychiatrists in the ER, querying their knowledge about catatonia prevalence, diagnostic modalities, and treatment strategies. ResultsAnalysis of 1118 patient records revealed a catatonia diagnosis rate of 0.36% (four cases) in the psychiatric ER. Remarkably, 75% of diagnosed patients were assessed by a single psychiatrist among nine available. Survey responses from all nine ER psychiatrists highlighted that 44% considered encountering catatonia as infrequent, while 33% regarded it as generally rare. Furthermore, 33% expressed willingness to employ potent antipsychotic interventions. ConclusionThe study revealed a markedly low catatonia detection rate in the psychiatric ER, coupled with inconsistent treatment approaches. Survey findings reflected a considerable proportion of psychiatrists holding outdated perceptions of catatonia.

Association between KRAS mutation and alcohol consumption in Brazilian patients with colorectal cancer

Colorectal cancer (CRC) is a leading cause of morbidity and mortality worldwide. Detection before metastasis and efficient treatment of disease significantly improve patient survival and quality of life. However, limitations in diagnosis and postoperative surveillance are associated with low CRC detection and survival rates. Thus, this project aimed to evaluate the molecular profile of patients diagnosed with CRC, as molecular biomarkers constitute a new frontier for diagnosis, treatment and prognosis. Methods and Results: 42 patients were included in the study, predominantly male (59.5%), with a median age of 63 years (SD: 10.0; min: 41; max: 83). The majority of primary tumors were located in the rectum (38.1%), in the sigmoid (33.3%) and in the ascending (21.4%) colon. We evaluated the genes KRAS, NRAS, BRAF, EGFR and TP53 using Sanger sequencing. Somatic and germline mutations were found in the KRAS, EGFR and TP53 genes, with the most common somatic alteration being rs121913529 in KRAS. This variant was also strongly associated with alcoholism (p = 0.002). Furthermore, patients with somatic mutations in TP53 had significantly higher mortality compared to those with wild-type alleles (OR: 11.2; 95% CI 1.25–2.45). Conclusions: Our findings support a relationship between alcohol consumption and the rs121913529 mutation, which is classified as pathogenic for colorectal cancer. Thus, further studies investigating the link between alcohol consumption, colorectal carcinogenesis and tumor progression ought to be conducted.

Towards optimized biopsy use in vertebral compression fractures: integrating risk assessment for better clinical decision-making.

Tissue biopsy is the gold standard for differentiating osteoporotic vertebral compression fractures from malignant lesions. However, the necessity of routine biopsies during percutaneous vertebroplasty and kyphoplasty is debated due to the low malignancy detection rates. This study aims to identify key predictors of positive biopsy outcomes in patients undergoing these procedures, with the goal of refining biopsy selection criteria to enhance diagnostic yield and improve clinical decision-making. We conducted a retrospective cohort study involving 295 patients who underwent percutaneous vertebroplasty and kyphoplasty with biopsy between June and December 2023. Clinical data, including age, gender, fracture aetiology, imaging findings, and biopsy results, were collected. Binary logistic regression analysis was employed to identify significant predictors of positive biopsy outcomes. The biopsy results revealed an overall malignancy rate of 1.01% (3/295). Among the 17 cases with abnormal (positive) biopsy findings, 17.6% were malignant, while the remainder were benign. Significant predictors included age (OR = 0.936), gender (OR = 0.307 for males), fracture aetiology (OR = 5.300 for fractures with no apparent cause), and imaging abnormalities (OR = 8.388). This study underscores the low malignancy detection rate in routine biopsies for vertebral compression fractures, advocating for a more selective approach by reserving biopsies for patients with specific high-risk factors. A targeted biopsy strategy, informed by enhanced pre-operative screening, could improve diagnostic accuracy and treatment outcomes, optimizing clinical management.

Target-site mediated insecticide resistance in major mosquito (Diptera: Culicidae) vectors: A systematic review

The use of insecticides remain the most effective vector control approach for mosquito borne diseases like malaria, dengue fever, chikungunya, and Zika virus disease; however, their increasing resistance has complicated the management. Mutations in kdr, ace-1/ace-2, rdl, and nAChR, are involved in target-site mediated resistance and prevent the binding of pyrethroids and dichlorodiph enyltrichloroethane, organophosphorus pesticide, carbamates and cyclodienes, respectively. Here, we review the current knowledge on target-site mediated insecticidal resistance mechanisms in major mosquito vectors and the techniques used to identify these mutations. The results showed that kdr mutations are frequently reported in mosquito species, indicating the overuse of pyrethroid in mosquito control. Report on ace mutations is very limited, primarily detected in Anopheles and Culex, with extremely low detection rate in Aedes, despite the wide application of organophosphates in their control. Notably, reports of rdl mutations and changes in nAChR are rare, which provides an opportunity to exploit these target sites, particularly in the event of disease outbreaks. Among various detection methods such as RFLP, TaqMan, Multiplex PCR and FRET, allele-specific PCR is mostly employed, especially in detecting kdr mutation. However, allele-specific PCR cannot detect novel mutation. As a result, there is an urgent need to develop a scalable, cost-effective tool that can be widely applied especially as these mutations are genetic markers for early detection of insecticide resistance. Robust monitoring methods remain critical to manage insecticide resistance and effective control of mosquito-borne diseases.

Factors affecting the accuracy of fetal cardiac ultrasound screening in the first trimester of pregnancy.

Most studies on the performance of first-trimester cardiac screening have concentrated on comparing the detection rate between different protocols and not on the actual reason for false-negative results. Herein, we report the performance of first-trimester congenital heart disease (CHD) screening and factors that may affect the detection rate of CHDs. This retrospective observational study included patients who underwent first-trimester screening and subsequently gave birth at our facility. We analyzed the performance of first-trimester screening for CHD and major CHD (CHD requiring cardiac surgery or interventional catheterization within 12months of birth). Of the 6614 fetuses included, 53 had CHD and 35 had major CHD. For the prenatal diagnosis of CHD, the detection rate, specificity, positive predictive value, negative predictive value, and first-trimester detection rate for CHD were 64.1%, 99.9%, 94.4%, 99.7%, and 82.9%, respectively; the respective values for major CHD were 85.7%, 99.96%, 93.75%, 99.92%, and 85.7%. The detection rate was not significantly different when classified by crown-rump length or number of fetuses. A weak correlation was observed between low detection rate of major CHD and lower maternal body mass index (BMI) (correlation ratio: 0.17). The detection rate was significantly higher when the fetus was scanned with its spine at the 5-7 o'clock position (posterior spine) than at other positions (odds ratio: 3.82, 95% confidence interval: 1.16-12.5, p = 0.02). Posterior spine contributes to an improved diagnostic rate in first-trimester CHD screening. In addition, sonographers must recognize that low maternal BMI is a risk factor of false-negative results.

Variation in the detection of lymphovascular invasion in T1 colorectal cancer and its impact on treatment: A nationwide Dutch study.

Lymphovascular invasion (LVI) plays an important role in determining the risk of lymph node metastasis (LNM) in T1 colorectal cancer (CRC) patients and influencing treatment decisions and patient outcomes. This study evaluated how the detection of LVI varies between Dutch laboratories and investigated its impact on the treatment and oncological outcomes of T1 CRC patients. Pathology reports and clinical data of T1 CRC patients who underwent local resection between 2015 and 2019 were obtained from the Dutch nationwide pathology databank (Palga cohort, n=5513). Data on the standard of LVI diagnosis (H&E/Immunohistochemistry) were not available. We categorized laboratories as low, average, or high detectors and evaluated the impact of LVI detection practice on the surgical resection rate and the proportion of LNM-negative (LNM-) surgeries. In the second part of the study, we used the Dutch T1 CRC Working Group cohort (n=1268) to evaluate the impact of LVI detection practice on cancer recurrences during follow-up. Multivariable logistic regression analyses and Cox proportional hazard regression were used to study the association between LVI detection practice and the outcomes. In the PALGA cohort, the proportion of surgical resections after local resection of a T1 CRC was significantly higher among patients diagnosed by laboratories with a high LVI detection rate (high vs. low: adjusted OR [aOR] 1.87; 95% confidence interval [CI] 1.52-2.31) as was the proportion of LNM-surgeries (aOR 1.73; 95% CI 1.39-2.15). In the second cohort, no significant difference was observed in cancer recurrences among patients diagnosed in laboratories with high detection rates compared with low detection rates (aHR 2.23; 95% CI 0.94-5.23). These findings suggest that a high detection rate of LVI does not improve oncological outcomes and may expose more patients to unnecessary oncological surgery, emphasizing the need for standardization of LVI diagnosis.

COMPARISON OF MACHINE LEARNING ALGORITHMS FOR DETECTION OF DATA EXFILTRATION OVER DNS

Nowadays, computers are indispensable for business processes and home users. The widespread use of the Internet provides convenience in many areas from education to research. However, most of the users are unaware of technical security measures and use the Internet unconsciously. This situation leads to inadequate security measures against cyber-attacks. Various trainings are organised for conscious and safe internet use, but these efforts are not enough. Therefore, artificial intelligence-based solutions that can detect cyber incidents and close security gaps are becoming necessary. DNS tunnelling is a method used by malware to leak data over the internet. Vulnerable computers can put users in difficult situations by learning IP addresses from the wrong DNS servers. Innovative methods have been developed to detect this tunnelling. Some methods can detect low and slow data leakage through DNS in real time. There are also hybrid DNS tunnelling detection systems that achieve high accuracy and F-score using packet length and specific features. Feature-based methods sensitive to cache characteristics effectively characterise DNS tunnelling traffic with low false detection rates. These methods offer effective strategies for internet security. In this study, the detection of DNS tunnelling attacks by machine learning algorithms on the CIC-Bell-DNS-EXF-2021 dataset was investigated.

EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection

Spam messages have emerged as a significant issue in digital communication, adversely affecting users’ mental health, personal safety, and network resources. Traditional spam detection methods often suffer from low detection rates and high false positives, underscoring the need for more effective solutions. This paper proposes the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, which integrates gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models utilizing a majority voting algorithm. The EGMA model enhances performance by incorporating additional statistical features extracted from message content and employing text vectorization techniques, such as Term Frequency–Inverse Document Frequency (TF-IDF) and CountVectorizer. The proposed model achieved impressive classification accuracies of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMl’s Twitter Spam dataset. These results demonstrate that the EGMA model outperforms individual models and existing methods in the literature, providing a robust solution for enhancing spam detection performance and effectively mitigating the threats that spam messages pose in digital communication.