Abnormal Categories Research Articles

Baseline Fasting Glucose Level, Age, Sex, and Body Mass Index and the Development of Diabetes in US Adults

Understanding the interplay between diabetes risk factors and diabetes development is important to develop individual, practice, and population-level prevention strategies. To evaluate the progression from normal and impaired fasting glucose levels to diabetes among adults. This retrospective community-based cohort study used data from the Rochester Epidemiology Project, in Olmsted County, Minnesota, on 44 992 individuals with at least 2 fasting plasma glucose (FPG) measurements from January 1, 2005, to December 31, 2017. People who met criteria for diabetes on or before their first FPG measurement were excluded. Data were electronically retrieved in December 2019 with analyses finalized in November 2024. The exposure was baseline FPG level, with covariates including the following measures that are consistently recorded in the electronic health record: body mass index (BMI), age, and sex. The cumulative probability of freedom from diabetes was estimated and presented graphically using a Kaplan-Meier curve. Multivariable Cox proportional hazards regression modeling was used to estimate the partial hazard ratios (HRs) for variables of interest. Diabetes was defined as an FPG level greater than 125 mg/dL. A total of 44 992 individuals (mean [SD] age at baseline, 43.7 [11.8] years; 26 025 women [57.8%]) were included. The baseline mean (SD) BMI was 28.9 (6.6). Over a median follow-up of 6.8 years (IQR, 3.6-9.7 years), 3879 individuals (8.6%) developed diabetes. The Kaplan-Meier 10-year cumulative risk of incident diabetes was 12.8% (95% CI, 12.4%-13.2%). All initial FPG levels outside a range of 80 to 94 mg/dL were associated with increased risk for diabetes (ie, FPG <70 mg/dL: HR, 3.49 [95% CI, 2.19-5.57]; FPG 120-125 mg/dL: HR, 12.47 [10.84-14.34]). Other independent risk factors were male sex (HR, 1.31 [95% CI, 1.22-1.40]), older age (≥60 years: HR, 1.97 [95% CI, 1.71-2.28]), and any abnormal category of BMI, including underweight (BMI <18.5: HR, 2.42 [95% CI, 1.77-3.29]; BMI ≥40: HR, 4.03 [95% CI, 3.56-4.56]). There was a significant additive association of variables, particularly FPG level and BMI. For instance, a woman aged 55 to 59 years with a BMI of 18.5 to 24.9 and an FPG level of 95 to 99 mg/dL had an estimated 10-year diabetes risk of 7.0%. However, an almost doubling of risk to 13.0% was observed if the BMI was 30.0 to 34.9, and risk more than doubled again to 28.0% if FPG level also increased to 105 to 109 mg/dL. A nomogram was generated to facilitate individual classification into one of four 10-year risk categories. This retrospective cohort study of 44 992 individuals suggests that FPG level, age, BMI, and male sex were all associated with development of diabetes, with significant interaction between these variables. These data contribute to understanding the clinical course of diabetes and highlight the substantial individual variation in diabetes risk according to commonly measured clinical variables. The findings facilitate lifestyle and pharmacologic interventions to treat those at highest risk of diabetes to reduce future morbidity and mortality. Further work is needed to validate this risk categorization tool for different populations.

Evaluating the pathological and clinical implications of errors made by an artificial intelligence colon biopsy screening tool

ObjectiveArtificial intelligence (AI) tools for histological diagnosis offer great potential to healthcare, yet failure to understand their clinical context is delaying adoption. IGUANA (Interpretable Gland-Graphs using a Neural Aggregator) is...

Internet addiction and social support in the association between learning burnout and emotional behavioral problems among returned migrant middle school students: a serial multiple mediations analysis

ObjectiveThe objectives of this study were to explore emotional behavioral problems (EBP) associated with learning burnout (LB) among returned migrant middle school students. In addition, the mediating mechanism of internet addiction (IA) and social support (SS) in this relationship was investigated. We also verified the chain-mediating effect of IA and SS on the EBP of returned migrant middle school students and LB.MethodsThis study used the sample data collected from the “Health Status Survey of Middle School Students in Guizhou Province, China” conducted by the research group. The baseline survey of 36 secondary schools from December 2021 to January 2022 in Guizhou Provine, China. Based on this sample data, middle school students who met the following conditions were selected as study subjects. 2020 returned migrant secondary school students were finally obtained as the subjects of this study. The Strength and Difficulty Questionnaire (SDQ), Adolescent Student Burnout Scale (ASBI), Internet Addiction Test (IAT), and Social Support Scale (SSS) were used to assess EBP, LB, IA, and SS among returned migrant secondary school students. Serial multiple mediation analysis and the bootstrap method were used to investigate the mediation effects of IA and SS (PROCESS model 6).ResultsPrevalence of EBP was classified in the abnormal (11.9%) or borderline (17.1%) categories based on the total difficulties scores among returned migrant middle school students. A serial multiple mediation model was generated. The mediation effect of IA and SS on the pathway from LB to EBP was 31.25% (direct effect = 0.373, total indirect effect = 0.170). The association between LB and SS was partially mediated by IA (direct effect=-0.356, indirect effect=-0.065). The proposed model fits the data well.ConclusionThis study found that lower IA and higher SS can effectively reduce EBP caused by LB among returned migrant middle school students. It is suggested that parents, schools, and government departments pay attention to IA and SS problems caused by LB in returned migrant middle school students, and prevent and avoid EBP. Returned migrant middle school students should take the initiative to identify the factors that harm their physical and mental health for self-regulation, and establish reliable SS, rather than meet psychological needs through IA, to reduce the occurrence of EBP.

Gender Analysis of Santri’s Mental Health in Islamic Boarding Schools

Mental health is a health issue that is often ignored. In fact, 1 in 3 adolescent in Indonesia experience mental health problems. Neglect is caused by unawareness of one's condition. Not to mention the problem of negative stigma in people with mental disorders. The purpose of this study was to determine the relationship between gender and mental health of students in Islamic boarding schools. This research method was quantitative with a cross-sectional design. The number of samples in this study was 357 male and female santri in Islamic boarding schools. The sampling technique used proportional random sampling. Data collection was carried out using the Strength and Difficulties Questionnaire (SDQ) with 25 questions referring to the technical instructions for health screening and periodic examinations of school-age children and adolescents. Data analysis was carried out using the chi square test. The results of the study showed that female santri had greater mental health problems, namely in the abnormal category, higher than male santri. The results of the chi square test showed a Pvalue &lt;0.05, meaning that there is a relationship between gender and mental health on the results of the SDQ difficulties, both in terms of emotional symptoms (&lt;0.001), behavioral problems (0.031), hyperactivity (&lt;0.001) and peers (&lt;0.001) and cumulatively (0.001). However, there is no relationship between gender and mental health on the results of the SDQ strength (0.074).

Multi-modal classification of breast cancer lesions in Digital Mammography and contrast enhanced spectral mammography images

Breast cancer ranks as the second most prevalent cancer in women, recognized as one of the most dangerous types of cancer, and is on the rise globally. Regular screenings are essential for early-stage treatment. Digital mammography (DM) is the most recognized and widely used technique for breast cancer screening. Contrast-Enhanced Spectral Mammography (CESM or CM) is used in conjunction with DM to detect and identify hidden abnormalities, particularly in dense breast tissue where DM alone might not be as effective. In this work, we explore the effectiveness of each modality (CM, DM, or both) in detecting breast cancer lesions using deep learning methods. We introduce an architecture for detecting and classifying breast cancer lesions in DM and CM images in Craniocaudal (CC) and Mediolateral Oblique (MLO) views. The proposed architecture (JointNet) consists of a convolution module for extracting local features, a transformer module for extracting long-range features, and a feature fusion layer to fuse the local features, global features, and global features weighted based on the local ones. This significantly enhances the accuracy of classifying DM and CM images into normal or abnormal categories and lesion classification into benign or malignant. Using our architecture as a backbone, three lesion classification pipelines are introduced that utilize attention mechanisms focused on lesion shape, texture, and overall breast texture, examining the critical features for effective lesion classification. The results demonstrate that our proposed methods outperform their components in classifying images as normal or abnormal and mitigate the limitations of independently using the transformer module or the convolution module. An ensemble model is also introduced to explore the effect of each modality and each view to increase our baseline architecture's accuracy. The results demonstrate superior performance compared with other similar works. The best performance on DM images was achieved with the semi-automatic AOL Lesion Classification Pipeline, yielding an accuracy of 98.85 %, AUROC of 0.9965, F1-score of 98.85 %, precision of 98.85 %, and specificity of 98.85 %. For CM images, the highest results were obtained using the automatic AOL Lesion Classification Pipeline, with an accuracy of 97.47 %, AUROC of 0.9771, F1-score of 97.34 %, precision of 94.45 %, and specificity of 97.23 %. The semi-automatic ensemble AOL Classification Pipeline provided the best overall performance when using both DM and CM images, with an accuracy of 94.74 %, F1-score of 97.67 %, specificity of 93.75 %, and sensitivity of 95.45 %. Furthermore, we explore the comparative effectiveness of CM and DM images in deep learning models, indicating that while CM images offer clearer insights to the human eye, our model trained on DM images yields better results using Attention on Lesion (AOL) techniques. The research also suggests a multimodal approach using both DM and CM images and ensemble learning could provide more robust classification outcomes.

Application of Whole-Exome Sequencing in the Prenatal Diagnosis of Foetuses With Central Nervous System Abnormalities.

To investigate the clinical value of whole-exome sequencing (WES) in the diagnosis of foetuses with central nervous system (CNS) abnormalities but having a normal karyotyping and chromosomal microarray result. During the period of 2016-2022, there were a total of 149 foetuses with CNS abnormalities but having negative karyotyping and chromosomal microarray analysis results; WES was performed on these foetuses and their parents. Variants were classified according to ACMG guidelines, and the association of pathogenic variants with specific types of CNS abnormalities was explored. Among these 149 foetuses, three categories of abnormalities, namely, single CNS abnormality, multiple CNS abnormalities, CNS abnormalities along with other organ system abnormalities were identified, for which the detection rate of P/LP variants is 17.4% (12/69), 28.6% (14/49) and 54.8% (17/31), respectively. WES brought about an increase of 28.9% in diagnostic yield in the prenatal evaluation of foetuses with CNS abnormalities but having negative karyotyping and chromosome array results. WES may also be of benefit for the diagnosis of foetuses with isolated CNS abnormalities, as well as for making more informed interpretations of imaging findings and for providing better genetic counselling.

Passive hearing abilities of dyslexia children: Investigating the rumble frequency of skull bones

Dyslexia is a specific learning disorder characterized with impairment of auditory frequency discrimination, impaired perception of amplitude, and modulation. The relation between sound discrimination and the passive hearing and sound conduction through the skull bone resonance cavities is still unclear. Methodology: 31 children diagnosed by dsm-5 as Dyslexia, sample collected from the dyslexia unit in( snsc) affiliated to, Asu.,along July 2022 to July 2023 with age range from 6 to 14 years, and both sexes were included. Material and apparatus: A calibrated 8 microphones for record and analysis at 63 Hz to 16 kHz via customised skull bone resonance cavity helmet. while seated in a room with RC25 chamber in a quiet laboratory Results: 31 dyslexic children age range 6_14, 22 males, and 9 females. normal bone resonance resulted in 8 children, while abnormal bone resonance was in 23 children. Two categories of abnormalities of the sound: (a) damping frequency, at a low/mid frequency ranging between 120 and 1000 Hz in 8 children. (b)rumble frequency, damping at a frequency ranging between 120 and 500 Hz, and further increase to range 1000 to 8000 Hz in 15 children. The sinuses sound abnormalities were in frontal and mastoid equally n = 21(67.7%), followed by maxillary n = 19(61.29%), more dominant on the right side. Conclusion: Investigating the skull bone resonance cavities can have good reflection on the sinuses condition, where further investigations will be mandatory and clinical intervention of the sinuses disease is to be essential.

Z-K-R: A Novel Framework in Intrusion Detection system through enhanced techniques

Abstract Intrusion detection systems (IDS) are an important tool for securing computer networks from various types of cyberattacks. The increasing complexity of network attacks demands more sophisticated approaches to intrusion detection. This paper presents an innovative method for IDS that involves combining Z-Score outlier detection, KMeans clustering, and Random Forest classification techniques. We tested our methodology using the CICIDS2017 dataset, which is a standardization dataset for intrusion detection that is frequently utilized. Our proposed approach first uses Z-Score outlier detection to identify abnormal traffic flows in the network. Next, KMeans clustering is used to group the traffic flows into different clusters based on their similarity. Finally, Random Forest classification is used to classify each traffic flow into normal or abnormal categories. Based on our experimental results, our approach for intrusion detection shows superior performance compared to several other state-of-the-art methods in terms of accuracy and precision. Our proposed method achieved an accuracy rate of 95.75% and a precision of 95.76%, surpassing the performance of KNN, SVM, and decision trees approaches. In conclusion, the proposed Z-K-R approach offers a promising solution for IDS by leveraging the strengths of Z-Score outlier detection, KMeans clustering, and Random Forest classification techniques. This strategy has the potential to increase the efficiency of IDS and boost network security in applications that take place in the real world.

Vascular Pathologic Conditions in and around the Spinal Cord.

Diagnosing and differentiating spinal vascular pathologic conditions is challenging. Small structures, lengthy imaging examinations, and overlapping imaging features increase the difficulty. Yet, subtle findings and helpful protocols can narrow the differential diagnosis. The authors aim to help radiologists make accurate and timely diagnoses of spinal vascular pathologic conditions in and around the spinal cord by highlighting spinal vascular anatomy, imaging findings, and three broad categories of abnormalities: infarcts, anomalies, and tumors. ©RSNA, 2024.

HRIDM: Hybrid Residual/Inception-Based Deeper Model for Arrhythmia Detection from Large Sets of 12-Lead ECG Recordings

Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons of death in the world. The timely, accurate, and effective prediction of heart diseases is crucial for saving lives. Electrocardiography (ECG) is a primary non-invasive method to identify cardiac abnormalities. However, manual interpretation of ECG recordings for heart disease diagnosis is a time-consuming and inaccurate process. For the accurate and efficient detection of heart diseases from the 12-lead ECG dataset, we have proposed a hybrid residual/inception-based deeper model (HRIDM). In this study, we have utilized ECG datasets from various sources, which are multi-institutional large ECG datasets. The proposed model is trained on 12-lead ECG data from over 10,000 patients. We have compared the proposed model with several state-of-the-art (SOTA) models, such as LeNet-5, AlexNet, VGG-16, ResNet-50, Inception, and LSTM, on the same training and test datasets. To show the effectiveness of the computational efficiency of the proposed model, we have only trained over 20 epochs without GPU support and we achieved an accuracy of 50.87% on the test dataset for 27 categories of heart abnormalities. We found that our proposed model outperformed the previous studies which participated in the official PhysioNet/CinC Challenge 2020 and achieved fourth place as compared with the 41 official ranking teams. The result of this study indicates that the proposed model is an implying new method for predicting heart diseases using 12-lead ECGs.

Artificial intelligence-derived left ventricular strain in echocardiography in patients treated with chemotherapy

Global longitudinal strain (GLS) is an echocardiographic measure to detect chemotherapy-related cardiovascular dysfunction. However, its limited availability and the needed expertise may restrict its generalization. Artificial intelligence (AI)-based GLS might overcome these challenges. Our aims are to explore the agreements between AI-based GLS and conventional GLS, and to assess whether the agreements were influenced by expertise levels, cardiac remodeling and cardiovascular diseases/risks. Echocardiographic images in the apical four-chamber view of left ventricle were retrospectively analyzed based on AI-based GLS in patients treated with chemotherapy, and correlation between AI-based GLS (Caas Qardia, Pie Medical Imaging) and conventional GLS (Vivid E9/VividE95, GE Healthcare) were assessed. The agreement between unexperienced physicians (“GLS beginner”) and experienced echocardiographer were also assessed. Among 94 patients (mean age 69 ± 12 years, 73% female), mean left ventricular ejection fraction was 64 ± 6%, 14% of patients had left ventricular hypertrophy, and 21% had left atrial enlargement. Mean GLS was − 15.9 ± 3.4% and − 19.0 ± 3.7% for the AI and conventional method, respectively. There was a moderate correlation between these methods (rho = 0.74; p < 0.01), and bias was − 3.1% (95% limits of agreement: -8.1 to 2.0). The reproducibility between GLS beginner and an experienced echocardiographer was numerically better in the AI method than the conventional method (inter-observer agreement = 0.82 vs. 0.68). The agreements were consistent across abnormal cardiac structure and function categories (p-for-interaction > 0.10). In patients treated with chemotherapy. AI-based GLS was moderately correlated with conventional GLS and provided a numerically better reproducibility compared with conventional GLS, regardless of different levels of expertise.

P-032 Prospective and multi-site analysis of pregnancy outcomes in 488 patients who completed the Sperm Quality Test (SpermQT) measuring the utility of sperm epigenetics

Abstract Study question Do SpermQT results prospectively correlate with fertility outcomes of couples undergoing intrauterine insemination (IUI)? Summary answer We observe a decrease in IUI pregnancy rates in men with Abnormal compared to Normal SpermQT results irrespective of their sperm total motile count (TMC). What is known already As the mainstay of male infertility diagnosis, the standard semen analysis provides limited value for predicting pregnancy outcomes. New technologies are needed for a more comprehensive assessment of male fertility. A novel, epigenetic assessment of sperm quality, SpermQT, was implemented clinically in 2022. Retrospective studies have shown that sperm health, as identified through DNA methylation patterns, is predictive of IUI outcomes in a subset of infertile men. Additional prospective research is needed for validation of these data. Study design, size, duration Over an 18-month period (June 2022 - January 2024), 23 reproductive endocrinologists over eight clinics ordered 488 SpermQT tests on male patients. For these patients, physicians and clinical support staff were asked to provide the sperm total motile count (TMC) from the semen analysis, male and female ages, procedure information and outcomes. SpermQT results were categorized as Normal or Abnormal. Associations between SpermQT results, outcomes, age, and TMC were analyzed. Participants/materials, setting, methods The selection of patients for SpermQT assessment was based on the clinical decision of the ordering physician. SpermQT requires a semen sample which was collected by the patient either at the andrology laboratory or at home using an at-home collection kit. All sample types were shipped directly to the vendor’s laboratory for analysis and report generation. Following the completion of the epigenetic analysis, all SpermQT results were provided to the ordering physician for patient consultation. Main results and the role of chance 488 patients received a SpermQT result. 83.6% of men had a Normal SpermQT (n = 408), and 16.4% had an Abnormal SpermQT (n = 80). 180 couples had pregnancy outcomes from at least one IUI. Of the 180, 14% of men had an Abnormal SpermQT (n = 25) and 86% had a Normal SpermQT (n = 155). Analysis of pregnancy outcomes from all 180 couples who completed one, two, or three IUI cycles showed a 15% IUI cumulative pregnancy rate. The men with a Normal SpermQT had a 17.42% cumulative IUI pregnancy rate within the first three IUI cycles while men with an Abnormal SpermQT (N = 25) had no reported pregnancies and a 0% cumulative IUI pregnancy rate. Within this patient cohort, 338 patients had reported TMC where 82% had a TMC ≥20 million (n = 276). 72% of men with an Abnormal SpermQT had a TMC ≥20M, while 84% of men with a Normal SpermQT had a TMC ≥20M. Median male and female ages in the Normal SpermQT category were both 34, while median male and female ages in the Abnormal SpermQT category were 32 and 33 respectively, indicating age did not play a role in IUI pregnancy outcomes. Limitations, reasons for caution Limitations within this study include the sample size of men with an Abnormal SpermQT result. In order to identify a statistical significance between IUI pregnancy outcomes, a total sample size of 500 patients will be needed. Data and outcomes will continue to be collected to continue the validation of SpermQT. Wider implications of the findings There is strong data showing that sperm epigenetics play an important role in IUI outcomes. Here we show in a prospective and multi-site population that a new assessment of sperm epigenetics (SpermQT) identifies men less likely to achieve an IUI pregnancy, and also identifies subfertile men with normal TMC. Trial registration number Non Applicable

Renal Cortical Imaging with Tc-99m DMSA in Children: An Institutional Review.

Background Urinary tract infection (UTI) is one of the commonly encountered conditions in children. Dimercaptosuccinic acid (DMSA) scintigraphy is widely advocated for functional and morphological evaluation of the renal cortex including parenchymal defect. Moreover, only a small percentage of renal defects are detected by ultrasound. We aimed to examine DMSA scintigraphy of children and identify factors associated with cortical defect. Methods Patients aged ≤ 18 years old who underwent DMSA scintigraphy (November 18, 2019-February 2, 2023, 30 children) were included. All children received intravenous injections of 99m Tc-DMSA followed by static planar and single-photon-emission computed tomography imaging at 3 hours. Cortical findings and differential functions of the worst affected kidney were graded accordingly. Grade I has no more than two cortical defects, grade II has more than two cortical defects with normal parenchyma between the defects, while grade III is when generalized damage is noted, and grade IV is when a shrunken kidney is seen with no DMSA uptake. Normal functioning kidney is when the relative function at 45 to 55%, mildly reduced function at 40 to 44%, and substantially impaired function at 10 to 39%, while nonfunctioning is when the differential split renal function < 10%. All data were then statistically analyzed. Results Majority was female (53%). The mean age was 5.85 years. UTI episodes were 73%. Twenty-two children had congenital urinary tract anomalies. All patients with vesicoureteric refluxes (VURs) had positive defects. Scintigraphy showed abnormalities in 17 children affecting unilateral (64%) or both kidneys (36%). There were 17 children (57%) respectively in the abnormal DMSA scan findings category with normal until significant impairment of the functioning kidney category. VURs were significantly associated with abnormal scintigraphy ( p < 0.05). A significant association was found between abnormal DMSA scan findings and differential renal function ( p < 0.05). Conclusion Significant association was noted between VURs and abnormal DMSA scintigraphy, abnormal DMSA scan findings, and impaired differential renal function. Special consideration should be given to these cases.

Maternal pre-pregnancy body mass index and related factors: A cross-sectional analysis from the Japan Environment and Children's Study.

Socioeconomic status and smoking are reportedly associated with underweight and obesity; however, their associations among pregnant women are unknown. This study aimed to investigate whether socioeconomic factors, namely educational attainment, household income, marital status, and employment status, were associated with pre-pregnancy body mass index (BMI) categories, including severe-moderate underweight (BMI ≤ 16.9 kg/m2), mild underweight (BMI, 17.0-18.4 kg/m2), overweight (BMI, 25.0-29.9 kg/m2), and obese (BMI ≥ 30.0 kg/m2) among Japanese pregnant women using data from the Japan Environment and Children's Study (JECS). In total, pregnant women were included 96,751. Age- and parity-adjusted multivariable multinomial logistic regression analyses assessed socioeconomic factors and smoking associations with falling within abnormal BMI categories (normal BMI as the reference group). Lower education and lower household were associated with overweight and obesity, and, especially, lowest education and household income had relatively higher point estimate relative ratios (RRs) of 3.97 and 2.84, respectively. Regarding the risks for underweight, however, only junior high school education had a significantly higher RR for severely to moderately underweight. Regarding occupational status, homemakers or the unemployed had a higher RR for severe-moderate underweight, overweight, and obesity. Unmarried, divorced, or bereaved women had significantly higher RRs for mildly underweight status. Quitting smoking early in pregnancy/still smoking had higher RRs for all four not having normal BMI outcomes; however, quitting smoking before pregnancy had a higher RR only for obese individuals. Lower educational attainment and smoking are essential intervention targets for obesity and severe-moderate underweight prevention in younger women. Lower household income is also a necessary target for obesity.

ReDIP, the Italian network for the diagnosis of congenital platelet function disorders

Congenital platelet function disorders (cPFD) are associated with an increased risk of mucocutaneous bleeding of various levels of severity; they may be classified based on abnormalities of platelet components that share common characteristics: i) platelet receptors for adhesive proteins; ii) platelet receptors for soluble agonists; iii) platelet granules; iv) signal transduction pathways; v) procoagulant phospholipids; less well characterized PFD aregrouped in a sixth category of miscellaneous abnormalities [...].

Evaluation of Developmental Dental Anomalies in an Adult Dentate Iraqi Sub-Population of Sulaimani City by Using Panoramic Radiographs

Objective: The present study attempts to evaluate the prevalence of developmental dental anomalies in patients aged 18 years and over by using panoramic radiographs among the Kurdish subpopulation in Sulaimani city.&#x0D; Methods: Seven hundred and ninety-four OPGs between 2010 and 2022 were evaluated retrospectively in this study. Using panoramicradiography to evaluate the frequency of dental anomalies in shape, structure, number, and size.&#x0D; Results: The prevalence of dental anomalies diagnosed by panoramic radiographs was 26.6% among the sample. Anomalies of shapewere the most common abnormalities, followed by number, size, and structure. The most common anomaly seen in this study wasdilaceration (18.52%), followed by hypodontia and microdontia (both 16.67%), taurodontism, and macrodontia (16.05.%). Lessfrequent sub-types were amelogenesis imperfecta, peg-shaped lateral incisors and ghost teeth, accounting for 0.62%.&#x0D; Conclusions: Tooth shape anomalies were the most prevalent forms of developmental dental defects among the Kurdish subpopulation in Sulaimani city. Dilaceration, hypodontia, microdontia and taurodontism were the most prevalent categories ofabnormalities. The study confirmed that racial variables play a role in the incidence of dental anomalies. In addition, digital panoramicradiography is a beneficial method for the detection of dental abnormalities. Importantly, patient management and the treatment design are dependent on an early diagnosis.

Interpretation of Platelet Histograms and Their Correlation with Peripheral Smear in Data Showing Thrombocytopenia

Objective: To analyze the significance of platelet histograms and peripheral smears in understanding thrombocytopenia and compare them for the incidence of pseudothrombocytopenia.Methods: This prospective study was conducted in the Department of Pathology at a tertiary care medical institute. The study included 200 cases of thrombocytopenia. Platelet parameters (PCT, PDW, MPV) were obtained using an autoanalyzer, and peripheral smears were examined manually. Thrombocytopenia cases were classified into hyper-destructive, hypo-productive, and abnormal pooling categories based on etiology and platelet histogram patterns. The incidence of pseudothrombocytopenia was also compared. A P value of less than 0.05 was considered statistically significant. Results: The gender distribution showed a male preponderance (56% male, 44% female). The mean age of the patients was 26.8 years. The study found hyper-destructive thrombocytopenia to be the most common type, with viral fever, sepsis, and malaria being the common etiologies for this type of thrombocytopenia. Histogram analysis revealed distinct patterns for different types of thrombocytopenia. The study also noted a higher incidence of pseudothrombocytopenia in automated analysis compared to manual methods, with a statistically significant difference.Conclusion: Platelet histograms combined with peripheral smear analysis provide crucial information about the etiology and nature of thrombocytopenia. This integrated approach enhances diagnostic accuracy and aid in effective patient management.

Abnormal line loss identification and category classification of distribution networks based on semi-supervised learning and hierarchical classification

Line loss refers to the electrical energy that is dissipated as heat during the transmission and distribution of electricity through power lines. However, unusual causes, such as grid topology mismatch and communication failure, can cause abnormal line loss. Efficient abnormal line loss detection contributes not only to minimizing energy wastage and reducing carbon emissions but also to maintaining the stability and reliability of the entire distribution network. In actual situations, the cause of abnormal line loss is not labeled due to the expensive labor cost. This paper proposes a hierarchical abnormal line loss identification and category classification model, considering the unlabeled and unbalanced sample problem. First, an abnormal line loss identification model-based random forest is established to detect whether the line loss is abnormal. Then, an abnormal line loss category classification model is developed with semi-supervised learning for line loss abnormal category classification, considering the unlabeled samples. The real dataset in China is utilized to validate the performance of the proposed model. Its reliability implies the potential to be applied to real-world scenarios to improve the management level and safety of the power grid.

Presence and utility of electrocardiographic abnormalities in long-term childhood cancer survivors

BackgroundWe assessed the prevalence and diagnostic value of ECG abnormalities for cardiomyopathy surveillance in childhood cancer survivors.MethodsIn this cross-sectional study, 1381 survivors (≥5 years) from the Dutch Childhood Cancer Survivor...

Relationship between Smartphone Use and Sleep Quality of Universitas Prima Indonesia Students

Smartphones, in the current era of digital globalization, have become a sophisticated communication tool. Missing the use of smartphones will affect personal sleep qualities, such as making sleep disorders worse and inefficient. Several cases of the negative impact of this gadget often affect children and adults. Starting from internet addiction, games, and content containing pornography, it is possible that it can also affect sleep patterns in children and adults. The aim of that research is to determine the relationships between smartphone use and the sleep quality of Universitas Prima Indonesia students. The typed research is quantitive within the cross-sectional approach. This population used was 200 semester 5 and 7 medical students at Prima Indonesia University. The samples technique at the studies used a total sample and the samples on 200 samples. Data analytics used univariate with bivariate analytics using Spearman rank statistics. The results of the research show that smartphone use among Universitas Prima Indonesia students is in the abnormal or high category, namely 97.5%. The sleep quality of the majority of Universitas Prima Indonesia students is in the quite poor category, with a percentage of 74.5%. This conclusion on that research is relationships between handphone use with the slep qualities Universitas Prima Indonesia students, p-value &lt;0.05.