Sensitivity Rate Research Articles

Clinical and pathogenic characteristics of infectious endophthalmitis in children: A retrospective analysis.

Infective endophthalmitis is an ophthalmic infection that in severe cases can cause complete loss of vision. In children, the defense against infection is low and eye tissue is not fully developed, leading to increased vulnerability to endophthalmitis. Children may be unable to understand the symptoms; thus, developing a method for prevention and treatment of this disease in children is important. Therefore, we analyzed the clinical and pathogenic characteristics of infectious endophthalmitis in children and provided evidence for clinical treatment. The clinical data of 78 children (78 eyes) with infectious endophthalmitis were retrospectively analyzed. The clinical characteristics, pathogen distribution, drug sensitivity, clinical medication, and treatments were summarized and analyzed. In total, 74 (94.87%) had ocular infections caused by trauma and 75 (96.15%) were from rural townships. A total of 108 sterile specimens were examined, with a positive detection rate of 37.04%. The sensitivity rates of Gram-positive cocci and bacilli to vancomycin were 100%. The sensitivity rates of Gram-negative bacilli to ceftazidime, piperacillin/tazobactam, amikacin, gentamicin, ciprofloxacin, and levofloxacin were 100%. Of the 78 patients, 53 (67.95%) received intravitreal injection and 54 (69.23%) underwent vitrectomy. Trauma is the main factor leading to infectious endophthalmitis in children, wherein Gram-positive bacteria are the most common pathogens. Thus, a timely understanding of the pathogen and drug sensitivity is needed. Intravitreal injection and vitrectomy are effective treatments.

Nanopore sequencing for smear-negative pulmonary tuberculosis—a multicentre prospective study in China

PurposeIn this prospective study, the diagnosis accuracy of nanopore sequencing-based Mycobacterium tuberculosis (MTB) detection was determined through examining bronchoalveolar lavage fluid (BALF) samples from pulmonary tuberculosis (PTB) -suspected patients. Compared the diagnostic performance of nanopore sequencing, mycobacterial growth indicator tube (MGIT) culture and Xpert MTB/rifampin resistance (MTB/RIF) assays.MethodsSpecimens collected from suspected PTB cases across China from September 2021 to April 2022 were tested then assay diagnostic accuracy rates were compared.ResultsAmong the 111 suspected PTB cases that were ultimately diagnosed as PTB, the diagnostic rate of nanopore sequencing was statistically significant different from other assays (P < 0.05). Fleiss’ kappa values of 0.219 and 0.303 indicated fair consistency levels between MTB detection results obtained using nanopore sequencing versus other assays, respectively. Respective PTB diagnostic sensitivity rates of MGIT culture, Xpert MTB/RIF and nanopore sequencing of 36.11%, 40.28% and 83.33% indicated superior sensitivity of nanopore sequencing. Analysis of area under the curve (AUC), Youden’s index and accuracy values and the negative predictive value (NPV) indicated superior MTB detection performance for nanopore sequencing (with Xpert MTB/RIF ranking second), while the PTB diagnostic accuracy rate of nanopore sequencing exceeded corresponding rates of the other methods.ConclusionsIn comparison with MGIT culture and Xpert MTB/RIF assays, BALF’s nanopore sequencing provided superior MTB detection sensitivity and thus is suitable for testing of sputum-scarce suspected PTB cases. However, negative results obtained using these assays should be confirmed based on additional evidence before ruling out a PTB diagnosis.

HTC-retina: A hybrid retinal diseases classification model using transformer-Convolutional Neural Network from optical coherence tomography images

Retinal diseases are among nowadays major public health issues, deservedly needing advanced computer-aided diagnosis. We propose a hybrid model for multi label classification, whereby seven retinal diseases are automatically classified from Optical Coherence Tomography (OCT) images. We show that, by combining the strengths of Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs), we can produce a more powerful type of model for medical image classification, especially when considering local lesion information such as retinal diseases. CNNs are indeed proved to be efficient at parameter utilization and provide the ability to extract local features and multi-scale feature maps through convolutional operations. On the other hand, ViT’s self-attention procedure allows processing long-range and global dependencies within an image. The paper clearly shows that the hybridization of these complementary capabilities (CNNs-ViTs) presents a high image processing potential that is more robust and efficient. The proposed model adopts a hierarchical CNN module called Convolutional Patch and Token Embedding (CPTE) instead of employing a direct tokenization approach using the raw input OCT image in the transformer. The CPTE module’s role is to incorporate an inductive bias, to reduce the reliance on large-scale datasets, and to address the low-level feature extraction challenges of the ViT. In addition, considering the importance of local lesion information in OCT images, the model relies on a parallel module called Residual Depthwise-Pointwise ConvNet (RDP-ConvNet) for extracting high-level features. RDP-ConvNet utilizes depthwise and pointwise convolution layers within a residual network architecture. The overall performance of the HTC-Retina model was evaluated on three datasets: the OCT-2017, OCT-C8, and OCT-2014 ; outperforming previous established models, achieving accuracy rates of 99.40%, 97.00%, and 99.77%, respectively ; and sensitivity rates of 99.41%, 97.00%, and 99.77%, respectively. Notably, the model showed high performance while maintaining computational efficiency.

Optimal Ensemble Transfer Learning Espoused Detection and Classification of Intracranial Haemorrhage

The injury of blood vessels in brain tissues known as intracranial haemorrhage (ICH) is a major cause of stroke. Computed tomography (CT) and X-ray images are frequently used to estimate the location and size of haemorrhages. Radiologists segment CT scan pictures manually using planimetry, which takes a lot of time. To overcome this issue, an optimal ensemble transfer learning espoused detection and classification of intracranial haemorrhage (CIH-V16-V19-R50-DN121-IV3-XN) is proposed in this paper. Initially, the images are gathered via the Radiological Society of North America (RSNA) ICH dataset. Afterwards, the images are fed to pre-processing. The pre-processed output is fed into a classification segment. The model used for classification is the ensemble transfer learning model such as VGG16, VGG19, Resnet50, InceptionV3, Desnet121, and XceptionNet. With the help of ensemble transfer learning techniques, the image is classified into subdural haemorrhage, epidural haemorrhage, subarachnoidal haemorrhage, intraventricular haemorrhage, intraparenchymal haemorrhage, and normal. With the help of voting classifier the best learning model with accuracy is identified. The proposed CIH-V16-V19-R50-DN121-IV3-XN algorithm is implemented in MATLAB using dataset of the RSNA ICH dataset. The performance metrics, such as accuracy, precision, sensitivity, F1-score, specificity, ROC, and error rate, are examined to analyse the efficiency of the proposed method. The performance of the CIH-V16-V19-R50-DN121-IV3-XN approach attains 10.12%, 22.33%, 21.45% high accuracy and 10.09%, 13.65%, 29.87% high specificity compared to the existing methods.

Evaluation of the passive mast cell activation test for identifying allergens in perioperative anaphylaxis: a study protocol for a prospective diagnostic accuracy study

IntroductionPerioperative anaphylaxis (POA) can lead to significant complications. Therefore, accurate identification of allergens for POA patients is critical to ensure the safety of future surgical and anaesthetic procedures. Existing perioperative...

Birefringence compensation method of test-mass substrates for gravitational wave detectors with arbitrary polarization states.

GW detectors are ultimately limited by thermal noise in their most sensitive region. Cryogenic operation combined with crystalline substrates and coatings is a promising approach to reduce this noise, thereby increasing their sensitivity and detection rate. However, crystalline materials can exhibit birefringent behaviors which will degrade the detector's sensitivity. Here, we demonstrate the use of a pair of identical electropolarization retarders to generate arbitrary polarization states and compensate birefringence of a KAGRA test-mass substrate.

Analysis of clinical characteristics and risk factors of chest tightness variant asthma in children

Objective: To analyze the clinical features and risk factors of chest tightness variant asthma (CTVA) in children, so as to provide basis for the prevention and management of the disease. Methods: A cross-sectional study was conducted to analyze 178 children aged 6-17 years old who were admitted to the Department of Allergy, Capital Institute of Pediatrics Affiliated Children's Hospital from January 2021 to January 2023 due to chest tightness. The age was 8.83(7.50, 11.58) years old, with 89 males (50%) and 89 females (50%). According to the diagnosis of CTVA, 130 cases were divided into CTVA group and 48 non-CTVA cases were divided into control group. Demographic data, personal history, family history, clinical features, auxiliary examination results and other data were collected. The clinical characteristics, allergens, FeNO level and pulmonary function parameters of the two groups were analyzed. Logistic regression analysis was used to explore the risk factors of the disease. Results: The proportion of school-age children (6-11 years old) in CTVA group was higher than that of adolescent children (≥12 years old) [(113/130,86.9%) vs (26/48,54.2%),Z=21.985,P<0.01]. The proportion of CTVA combined with eczema [(74/130,56.9%) vs (19/48,39.6%), χ2=4.225,P<0.05] and rhinitis symptoms [(98/130,75.4%) vs (27/48,56.2%), χ2=6.138,P<0.05] was higher. The positive rates of mold sensitization [(52/130,40.0%) vs (11/48,22.9%), χ2=4.474,P<0.05] and multiple sensitization [(71/130,54.6%) vs (18/48,37.5%), χ2=4.108,P<0.05] in inhaled allergens were significantly higher than those of control group. The proportion of elevated FeNO (>20 ppb) in CTVA children was 20.8% (27/130), which was significantly higher than that in control group 4.2%(2/48)(χ2=7.086,P<0.01). There were no statistical differences in spirometry parameters FEV1 and FVC between CTVA group and control group (P both>0.05). FEV1/FVC, PEF, FEF25, FEF50, FEF75 and MMEF were significantly lower than those in control group (P all<0.05). Logistic regression analysis showed that rhinitis symptoms (OR=2.351, 95%CI 1.105-5.002, P=0.026), multiple sensitizations (OR=2.184, 95%CI 1.046-4.557, P=0.038), tIgE>60 kU/L(OR=3.080, 95%CI 1.239-7.654, P=0.015), FeNO>20 ppb (OR=6.734, 95%CI 1.473-30.796, P=0.014) and small airway dysfunction (OR=3.164, 95%CI 1.089-9.194, P=0.034) were risk factors for chest tightness variant asthma. FeNO combined with FEF50 has the largest area under the curve (Z=2.744, P<0.01) in diagnosing CTVA. Conclusion: CTVA is more common in school-age children than in adolescent children. Rhinitis symptoms, multiple sensitization, tIgE>60 kU/L, FeNO>20 ppb and small airway dysfunction are risk factors for chest tightness variant asthma. FeNO combined with small airway indexes can improve the diagnostic value of CTVA.

Regression–Based Diagnostic Models for Early Lung Cancer Integrating Conventional Indicators with Tumor Markers

The aim of this research was to develop a lung cancer diagnostic and predictive model that integrates traditional laboratory indicators with tumor markers. This model is intended to facilitate early screening and assist in the process of identifying or detecting lung cancer through a cost-effective, rapid, and convenient approach, ultimately enhancing the early detection rate of lung cancer. A retrospective study was conducted on 66 patients diagnosed with lung cancer and 159 patients with benign pulmonary conditions. Data including general clinical information, conventional laboratory test results, and tumor marker levels were collected. Data analysis was conducted using SPSS 26.0 (Statistical Product and Service Solutions 26.0). The lung cancer diagnosis and prediction model is created using a composite index established through binary logistic regression. The combined diagnostic prediction models, incorporating both traditional indicators and tumor markers, demonstrated a greater area under the curve (AUC) when compared to the diagnostic prediction model based solely on tumor markers and their combination testing. The values of cut-off point, AUC, accuracy, sensitivity, specificity, positive and negative detection rate and accuracy rate are 0.1805, 0.959, 86.67%, 0.955, 0.830, 95.45%, 83.02% and 89.33 respectively and it is shown that the combined diagnostic model display notable efficacy and clinical relevance in aiding the early diagnosis of lung cancer.

Advancement on target ranging and tracking by four-quadrant photon counting lidar.

Single-photon lidar stands out as a promising technology for long-distance lidar applications, owing to its attributes of single-photon sensitivity and high repetition rate. Existing single-photon lidar systems typically rely on single-point scanning for positioning and tracking, necessitating intricate and precise scanning control. In pursuit of a more concise and efficient positioning, we incorporate the four-quadrant theory to articulate the signal formula of photon detection, and propose a novel single-photon four-quadrant positioning method. Our method, which includes signal preprocessing, compensation for longitudinal motion, extraction of pixel intensity, and acquisition of lateral motion, facilitates motion acquisition and positioning for targets. Through simulation calculations, we analyze and compare the effectiveness of each step of the method. With longitudinal and lateral speeds of 100 m/s and 50 m/s, respectively, the trajectory error is 1.7%, and the average speed error is 1.8%. Moreover, for various verification experiments, the trajectory errors are all below 4.2%, and the average speed errors remain under 5.4%, effectively verifying the validity of our method in acquiring the motion information and positioning of targets. It provides an excellent option for acquiring motion information and tracking small moving targets over long distances.

Detection of short-lasting and ictal spike-and-wave discharges in around-the-ears EEG recordings in children with absence epilepsy

PurposeLong-term ambulatory EEG recordings can improve the monitoring of absence epilepsy in children, but signal quality and increased review workload are a concern. We evaluated the feasibility of around-the-ears EEG arrays (cEEGrids) to capture 3-Hz short-lasting and ictal spike-and-wave discharges and assessed the performance of automated detection software in cEEGrids data. We compared patterns of bilateral synchronisation between short-lasting and ictal spike-and-wave discharges. MethodsWe recruited children with suspected generalised epilepsy undergoing routine video-EEG monitoring and performed simultaneous cEEGrids recordings. We used ASSYST software to detect short-lasting 3-Hz spike-and-wave discharges (1–3 s) and ictal spike-and-wave discharges in the cEEGrids data. We assessed data quality and sensitivity of cEEGrids for spike-and-wave discharges in routine EEG. We determined the sensitivity and false detection rate for automated spike-and-wave discharge detection in cEEGrids data. We compared bihemispheric synchrony across the onset of short-lasting and ictal spike-and-wave discharges using the mean phase coherence in the 2–4 Hz frequency band. ResultsWe included nine children with absence epilepsy (median age = 11 y, range 8–15 y, nine females) and recorded 4 h and 27 min of cEEGrids data. The recordings from seven participants were suitable for quantitative analysis, containing 82 spike-and-wave discharges. The cEEGrids captured 58 % of all spike-and-wave discharges (median individual sensitivity: 100 %, range: 47–100 %). ASSYST detected 82 % of all spike-and-wave discharges (median: 100 %, range: 41–100 %) with a false detection rate of 48/h (median: 6/h, range: 0–154/h). The mean phase coherence significantly increased during short-lasting and ictal spike-and-wave discharges in the 500-ms pre-onset to 1-s post-onset interval. ConclusionscEEGrids are of variable quality for monitoring spike-and-wave discharges in children with absence epilepsy. ASSYST could facilitate the detection of short-lasting and ictal spike-and-wave discharges with clear periodic structures but with low specificity. A similar course of bihemispheric synchrony between short-lasting and ictal spike-and-wave discharges indicates that cortico-thalamic driving may be relevant for both types of spike-and-wave discharges.

The role of HOTAIR in the modulation of resistance to anticancer therapy.

Leading anti-tumour therapeutic strategies typically involve surgery and radiotherapy for locally advanced (non-metastatic) cancers, while hormone therapy, chemotherapy, and molecular targeted therapy are the current treatment options for metastatic cancer. Despite the initially high sensitivity rate to anticancer therapies, a large number of patients develop resistance, leading to a poor prognosis. The mechanisms related to drug resistance are highly complex, and long non-coding RNAs appear to play a crucial role in these processes. Among these, the lncRNA homeobox transcript antisense intergenic RNA (HOTAIR), widely implicated in cancer initiation and progression, likewise plays a significant role in anticancer drug resistance. It can modulate cell activities such as proliferation, apoptosis, hypoxia, autophagy, as well as epithelial-mesenchymal transition, thereby contributing to the development of resistant tumour cells. In this manuscript, we describe different mechanisms of antitumor drug resistance in which HOTAIR is involved and suggest its potential as a therapeutic predictive biomarker for the management of cancer patients.

Sensitivity of Klebsiella Pneumoniae Bacteria Isolated from the Urine of Patients with Urinary Tract Infections in Kirkuk City/ Iraq

This study was conducted to investigate the presence of Klebsiella pneumoniae bacteria causing urinary tract infections among patients suffering from urinary infections, to isolate and test their antibiotic sensitivity in order to identify the effective antimicrobial agent against them. A total of 136 urine samples were collected from individuals with urinary tract infections, aged 2 to 75 years, during the period from August 6, 2023, to November 16, 2023. The samples were cultured on MacConkey agar and blood agar. A positive growth was exhibited in 65 samples, accounting for 47%, while 71 samples did not show any bacterial growth, making up 52%. Out of these, 17 isolates were identified as Klebsiella, with a positivity rate of 26%, diagnosed through microscopic, cultivation, and biochemical tests. The study results indicated that Klebsiella pneumoniae was one of the most common pathogenic causes after E.coli bacteria. The Incidence of Klebsiella was higher in females than in males, with most infections occurring in married women more than in single women. Klebsiella bacteria showed a high resistance of 100% against Gentamycin, Rifampicin, Tetracycline, Ampicillin, and Cefoxitin, while the resistance rates varied for Imipenem, Tobramycin, Trimethoprim, with 76%, 52%, and 88% respectively. The sensitivity rates ranged for Chloramphenicol, Ofloxacin, Amikacin, and Azithromycin with 41%, 35%, 11%, and 17.

Association between frozen embryo transfer and childhood allergy: a retrospective cohort study

Research questionDoes frozen embryo transfer (FET) elevate the risk of allergic diseases in offspring? DesignIn this study, we followed up 653 singleton children, including 166 children born through FET and 487 children born through natural conception (NC). Demographic characteristics, perinatal information and allergic diseases of children and their parents were collected through clinical medical systems and questionnaires. Furthermore, among these 653 children, allergen-specific IgE testing was performed using peripheral blood samples collected from 207 children, including 62 children in the NC group and 145 children in the FET group. The prevalence rates of allergic diseases and the positive rates of allergen-specific IgE testing were compared between FET and NC groups with adjustments for confounding factors. ResultsResults showed that children born from FET had a significantly elevated prevalence rate of food allergy (aOR=3.154, 95% CI:1.895-5.250, p<0.001). Align with this, the positive rates of food allergen sensitization were also increased (aOR=5.767, 95%CI: 2.859-11.751, p<0.001). In addition, FET children exhibited a higher positive sensitization rate to at least one allergen compared to NC children (aOR=3.127, 95%CI: 1.640-5.961, p<0.001). No association was observed between FET and other allergic diseases, including asthma (p=0.136), atopic dermatitis (AD) (p=0.130) and allergic rhinitis (AR) (p=0.922). Also, allergen-sensitization IgE testing indicated no differences between FET and NC children in the positive rates of other common allergens, including animals and insects allergens (p=0.627), inhaled outdoor allergens (p=0.915), and inhaled outdoor allergens (p=0.544). ConclusionOur study suggested that children born from FET had an increased risk of developing food allergies in early childhood.

Construction and validation of a postoperative hypothermia prediction model in elderly patients undergoing colorectal surgery

BackgroundPostoperative hypothermia (POH) is a common issue in colorectal surgery patients, leading to complications. This study aimed to develop and validate a predictive model for identifying POH in colorectal surgery patients. MethodsA retrospective analysis of 1,316 patients who underwent colorectal surgery between June 2020 and September 2022 was conducted using institutional medical records. Intraoperative core temperatures and potential influencing factors were collected, and regression analysis was used to identify risk factors for POH and create a model. The model's performance was evaluated using the receiver operating characteristic curve analysis. ResultsIntraoperative hypothermia occurred in 51.5 % of patients. Significant predictors of POH included gender, alcohol consumption, surgery duration, platelet count, and age. The constructed model included factors like fluid intake, platelets, cigarette use, alcohol consumption, surgery type, muscle relaxants, age, ABSI, and gender. The model showed good predictive performance with an area under the ROC curve of 0.981 and a Hosmer-Lemeshow test p-value of 0.676. The Youden index, sensitivity, specificity, and practical application rate were 0.602, 0.790, 0.812, and 98.81 %, respectively. ConclusionThis study developed a predictive model for POH in colorectal surgery patients, considering individual factors and exploring underlying causes. Understanding risk factors and consequences of POH is crucial for nurses and perioperative professionals in clinical practice.

A digital image smartphone-based approach to Slovak Tokaj wine authentication chemometric assisted

The authentication of Slovak wines in comparison to other similar wines from various geographical regions, namely Hungary, France, Austria, and Ukraine, was conducted using the OC-PLS, DD-SIMCA, and PLS-DM models, all of them operating in rigorous way. The study involved 63 samples, of which 41 originated from Slovakia, covering diverse wine types such as varietal wines, cuvée selections (different “putňový”), and essence. To capture digital images under controlled conditions, a custom-made cardboard box with white inner surfaces was devised and equipped with a smartphone. During the training phase, sensitivities of 96%, 100%, and 96% were attained for OC-PLS, DD-SIMCA, and PLS-DM, respectively. In the subsequent stages of validation and testing for DD-SIMCA and PLS-DM, the proposed methods displayed optimal efficiency, achieving both sensitivity and specificity rates of 100%. However, such results were not achieved in the case of OC-PLS, which exhibited efficiency levels of 90% in validation and 80% in testing.

Hybrid wavelength selection strategy combined with ATR-FTIR spectroscopy for preliminary exploration of vintage labeling traceability of sauce-flavor baijiu

The allure of substantial profits has perpetuated the illicit trade of counterfeit vintage labels for baijiu. While various approaches have been employed to intelligently ascertain the vintage of baijiu, many of them are both cost-intensive and time-consuming. This work pioneered the use of Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric analysis, offering a non-destructive and economically viable method for discriminating sauce-flavor baijiu across different aging periods (1-, 2-, and 3-year). In this research, principal component analysis (PCA) was first conducted to explore clustering trends among distinct vintage groups. Subsequently, the effect of spectral pre-processing on modeling performance was explored. For wavelength selection, four wavelength selection methods (ReliefF, random forest variable importance (RFVI), variable importance in projection (VIP), and Venn) were first used to identify the subset of candidate features that potentially best mapped the vintage labels. Immediately following this, to explore the possibility of further improving the identification capabilities of the model as well as to reduce the redundant data that may still be present, sequential backward selection (SBS) was utilized for secondary feature reduction within the subset of candidates. The amalgamation of these two techniques is termed a “hybrid wavelength selection strategy.” Additionally, the dimensionality reduction effects of PCA and kernel principal component analysis (KPCA) were compared to demonstrate the robustness of the proposed method. Finally, classification models such as partial least squares discriminant analysis (PLS-DA), random forest (RF), and grasshopper optimization algorithm-based support vector machine (GOA-SVM) were developed. The results show that the spectral data need not be pre-processed, and the proposed hybrid wavelength selection strategy can further improve the identification ability of the model. Among the many models developed, ReliefF-SBS-GOA-SVM emerged as the most proficient classification model, yielding accuracy, sensitivity, and specificity rates of 94.44%, 95.23%, and 94.44%, respectively. This method not only holds promise for the discrimination of baijiu class attributes such as brand, origin, flavor, and vintage but also exhibits potential applicability in other non-targeted identification studies involving spectroscopy methodologies.

High Serum Allergen-Specific IgE of House Dust Mite in Predicting the Risk of Comorbidity in Children with Allergic Conjunctivitis.

To investigate the patterns of allergens in allergic conjunctivitis (AC) and the association with allergic comorbidity. This retrospective cross-sectional study enrolled 2972 children with AC. Clinical data, including sex, age, allergic comorbidities (allergic asthma, allergic rhinitis, and atopic dermatitis), and serum allergen-specific immunoglobulin E (sIgE), were collected from the electronic medical record (EMR). The categorical variables were compared with the chi-square test. The characteristics of allergens in children of different ages and comorbidities were analyzed by trend chi-square. The sensitivity level of HDM associated with AC and comorbidities was assessed by odds ratios (ORs) with 95% confidence intervals of logistic regression analysis. A total of 2972 children (2015 boys and 957 girls) with AC were included in the study. The mean age was 3.78 (0.5~12) years. The most common allergen was house dust mite(HDM) (43.41%). With age, the positive rate for inhaled allergens gradually increased, and the positive rate for ingested allergens decreased. Withthe number of comorbiditiesincreasing, the positive rates of sensitization were 38.33%, 74.51%, 80.72%, and 89.05%, and the incidence of polysensitization was 44.66%, 56.48%, 59.54%, and 74.59%, respectively. With the increase of HDM-sIgE level, the number of comorbidities and the risk increased gradually. HDM is the most common allergen in AC children of different ages. High levels of HDM-sIgE may be a predictor for allergic comorbidities. Children with polysensitization and high levels of HDM sIgE will be an important target population for future intervention in other allergy-related disease prevention.

Evaluation of cost-effective multiple cancer early detection with extremely low coverage whole genome sequencing from plasma.

10538 Background: Cancer continues to be a significant global health concern, with approximately 19.3 million new cases diagnosed and 10.0 million cancer-related deaths reported in 2020. Multi-cancer early detection (MCED) test using the peripheral blood offers a great opportunity to improve the current cancer screening tests with better performance and benefit-to-harm balance. Methods: Here we introduce a novel MCED test called HIFI-PROF, which utilizes extremely low pass whole-genome sequencing (median coverage of 0.6X) to create a multi-dimensional fragmentation signatures model through machine learning approach. HIFI-PROF was developed to detect and localize six types of cancer (colorectal, pancreas, liver, lung, esophagus and gastric cancer). Results: The study included a total of 1,487 participants, comprising 235 colorectal cancers, 227 pancreatic cancers, 150 liver cancers, 125 lung cancers, 107 esophageal cancers, 106 gastric cancers and 537 healthy volunteers. Most of the cancer cases were I/II stage patients (stage I: 38%, stage II: 35%, stage III: 23%, stage IV: 4%). The participants, stratified by age and clinical status, were divided into two sets: a training set (n=1,079) and a validation set (n=408).HIFI-PROF exhibited an excellent performance in the early detection of multiple cancer types, achieving a sensitivity of 87.58% at a specificity of 99.09% and identifying the tissue of origin (TOO) with an accuracy of 81.99%. The sensitivity rates for detecting cancer at different stages were also promising, with a sensitivity of 84.62% for stage I, 85.59% for stage II, 89.61% for stage III and 92.86% for stage IV. Conclusions: These results highlight the high sensitivity and specificity of HIFI-PROF in identifying cancer cases while maintaining a low rate of false-positive results. The use of HIFI-PROF offers a cost-effective MCED test with excellent performance. Such advancements will contribute to improve strategies for multi-cancer early detection, leading to more effective clinical interventions and better outcomes.

Deciphering the correlation between Chr3q26.2 amplification through homologous recombination deficiency and improved survival in ovarian cancer under platinum-based adjuvant therapy.

e17537 Background: Discriminant copy number alterations (CNAs) have been linked to aberrant DNA repair mechanisms. This study aimed to identify CNA-based biomarkers, alongside homologous recombination deficiency (HRD) scores for ovarian cancer patients receiving platinum (Pt)-based chemotherapy, especially in BRCA1/2 wild-type or HRD-low patients. Methods: Fourteen most prevalent and significant focal CNA regions (seven amplification and seven deletion) were examined to investigate their correlations with HRD scores via SHAP values of an XGBoost model and the effectiveness of Pt-based adjuvant chemotherapy in a cohort of high-grade serous ovarian cancer (HGSC) patients (TCGA cohort, N=576), and the results were validated in a separate Chinese ovarian cancer cohort (test cohort, N=457). An in-house developed next-generation sequencing HRD pipeline (GeneseeqPrime HRD) was used to determine HRD scores. Histological subtypes, molecular features, and potential molecular mechanisms associated with the identified biomarkers were further explored. Results: Of 14 high-level CNA regions identified by GISTIC in the TCGA cohort, Chromosome 3q26.2 amplification [ Chr3(q26.2) Amp, a prevalence of 30.7%] was associated with increased HRD scores in both BRCA1/2-altered ( p=0.01) and wild-type HGSCs ( p=0.03). Consistently, in the test cohort, higher HRD scores ( p&lt;0.01) and elevated chromosome instability scores ( p&lt;0.01) were observed in Chr3(q26.2) amplified samples. Among 406 patients administrated with Pt-based adjuvant chemotherapy in the TCGA cohort, those with Chr3(q26.2) Amp exhibited a relatively high sensitivity rate ( p=0.09) and significantly superior disease-free survival after adjusting for HRD score, patient age, tumor stage, and residual disease [hazard ratio (HR): 0.65, 95% confidence interval (CI): 0.48–0.88] when compared to those without, representing Chr3(q26.2) Amp as an independent predictive biomarker. Similar results were obtained in 78 patients using Pt-based adjuvant chemotherapy in the test cohort (HR: 0.68, 95% CI: 0.40–1.15). Moreover, Chr3(q26.2) Amp was more prevalent in HGSCs harboring BRCA1/2 or TP53 aberrations than other histological subtypes driven by PIK3CA, PTEN or KRAS mutations ( p&lt;0.01). In comparison to HGSCs without Chr3(q26.2) Amp, Chr3(q26.2) amplified HGSCs exhibited higher Ragnum hypoxia scores ( p&lt;0.01) and a total of four overexpressed proteins, including p62 ( p&lt;0.01), PIK3CA ( p&lt;0.01), CCNB1 ( p&lt;0.01) and TSC1 ( p&lt;0.01), indicative of suppressed homologous recombination repair pathway and cycle cell arrest. Conclusions: Chr3(q26.2) Amp emerges as a promising biomarker for Pt-based adjuvant chemotherapy in ovarian cancer, potentially reflecting a hypoxia-induced etiology through suppressed homologous recombination repair pathway.

Development of a machine learning-based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population.

This study aimed to evaluate the effectiveness of a random forest (RF) model in predicting clinical pregnancy outcomes from intrauterine insemination (IUI) and identifying significant factors affecting IUI pregnancy in a large Chinese population. RESULTS: A total of 11 variables, including eight from female (age, body mass index, duration of infertility, prior miscarriage, and spontaneous abortion), hormone levels (anti-Müllerian hormone, follicle-stimulating hormone, luteinizing hormone), and three from male (smoking, semen volume, and sperm concentration), were identified as the significant variables associated with IUI clinical pregnancy in our Chinese dataset. The RF-based prediction model presents an area under the receiver operating characteristic curve (AUC) of 0.716 (95% confidence interval, 0.6914-0.7406), an accuracy rate of 0.6081, a sensitivity rate of 0.7113, and a specificity rate of 0.505. Importance analysis indicated that semen volume was the most vital variable in predicting IUI clinical pregnancy. The machine learning-based IUI clinical pregnancy prediction model showed a promising predictive efficacy that could provide a potent tool to guide selecting targeted infertile couples beneficial from IUI treatment, and also identify which parameters are most relevant in IUI clinical pregnancy.