Digital Examination Research Articles

A novel method to detect carotid artery in-stent restenosis.

Carotid artery stenosis is a major risk factor for ischemic stroke. Despite carotid artery stenting, in-stent restenosis (ISR) remains challenging. Pigs serve as an ideal ISR model. This study aims to establish a novel porcine model of carotid ISR using open-loop and closed-loop stents and to assess ISR with optical coherence tomography (OCT) and histopathology, comparing incidence and vascular response between stent types. Twelve adult male Bama miniature pigs underwent carotid stenting with either open-loop or closed-loop stents. The animals received antiplatelet therapy pre- and postimplantation. Postimplantation evaluations at 90days included carotid digital subtraction angiography (DSA), OCT, histopathological examination, and electron microscopy. Both stent types showed ISR as detected by OCT and DSA. OCT revealed comparable neointimal proliferation within stent struts for both types, with no significant differences in stent, lumen, and neointimal dimensions. Histopathological analysis and electron microscopy provided insights into tissue responses and healing processes following stent implantation. No significant difference in ISR incidence was found between the stent types based on a χ2 test (p=.110). OCT and hematoxylin-eosin staining exhibit the highest consistency in evaluating neointimal area. The novel porcine ISR model demonstrated similar ISR outcomes for open-loop and closed-loop stents. OCT proved to be a highly consistent and valuable tool for evaluating stent and arterial conditions, comparable to histopathological findings. However, due to a small sample size, the validity of these preliminary findings requires further investigation to be confirmed.

The System of Forensic Activity Digitalization Theory

The article examines the system of private theory of forensic activity digitalization from the standpoint of forensic expertology. The subject, objects, tasks of the theory and its place in forensic expertology are described. The paper presents the theory of forensic activity digitalization that can be attributed to several private theories, the provisions of which equally apply both to the process of expert examination in general and to expert studies of individual types of examinations as well. Two sections in the theory system are designated: forensic examination of digital footprints and information and computer support for forensic activity. The first section includes and considers the following: the nature of digital footprints, their properties and features and the mechanism of formation; forms of presentation and classification of digital footprints and their carriers as objects of forensic examinations; general tasks of digital footprints expert examination. The second section pays attention to the technologies of algorithmizing and digitalization of methods and techniques of forensic examination. The prospects for the introduction of neural networks to forensic examination and the related relevant problems are considered. In addition, the author notes changes in the methodology and technologies for developing expert methods in connection with the introduction of artificial intelligence algorithms. This section outlines the areas of application of neural networks for solving forensic problems. The reasons for erroneous conclusions related to application of neural networks in forensic examinations are analyzed. Particular attention is paid to the sources of model risk. To develop methods for solving typical expert problems based on neural networks it is proposed to create forensic datasets and repositories for consequent analysis and machine learning for various types of forensic examinations. The article also substantiates the need for new expert competencies: expert data analyst, expert data engineer and machine learning engineer.

Intrapartum Ultrasound Guidance to Make Safer Any Obstetric Intervention: Fetal Head Rotation, Assisted Vaginal Birth, Breech Delivery of the Second Twin.

Intrapartum ultrasound (US) is more reliable than clinical assessment in determining parameters of crucial importance to optimize the management of labor including the position and station of the presenting part. Evidence from the literature supports the role of intrapartum US in predicting the outcome of labor in women diagnosed with slow progress during the first and second stage of labor, and randomized data have demonstrated that transabdominal US is far more accurate than digital examination in assessing fetal position before performing an instrumental delivery. Intrapartum US has also been shown to outperform the clinical skills in predicting the outcome and improving the technique of instrumental vaginal delivery. On this basis, some guidelines recommend intrapartum US to ascertain occiput position before performing an instrumental delivery. Manual rotation of occiput posterior position (MROP) and assisted breech delivery of the second twin are other obstetric interventions that can be performed during the second stage of labor with the support of intrapartum US. In this review article we summarize the existing evidence on the role of intrapartum US in assisting different types of obstetric intervention with the aim to improve their safety.

Predicting Vaginal Delivery Success: Role of Intrapartum Transperineal Ultrasound Angle of Descent at a Single Center.

BACKGROUND Assessment of labor progress via digital vaginal examination is subjective, while intrapartum transperineal ultrasonography is deemed more objective and effective. The angle of descent (AoD) is a reliable intrapartum transperineal ultrasound parameter with minimal inter-observer and intra-observer errors. This study compared intrapartum transperineal ultrasonography with digital vaginal examination in evaluating the angle of descent for vaginal delivery selection in 70 women. MATERIAL AND METHODS This cross-sectional study was conducted at Dr. Mohammad Hoesin Hospital Palembang in Indonesia, using a consecutive sampling method to measure AoD of 70 patients at admission and every 2 h during labor progression. Patients were monitored until childbirth, and outcomes of successful vaginal or cesarean delivery were reported. A diagnostic test determined the optimal AoD for predicting successful vaginal delivery and its predictive value. RESULTS The study included 70 patients with a mean age of 28.29±6.09 years and a body mass index of 30.59±3.49 kg/m². Among them, 29 (41%) had successful vaginal deliveries. Adequate contraction frequency at admission was strongly associated with successful vaginal delivery (P<0.001). The optimal AoD for predicting successful vaginal delivery was 94.5°, with an area under the curve of 0.567 (95% CI 0.425-0.710), sensitivity of 84.2%, specificity of 45.7%, and accuracy of 54.3%. CONCLUSIONS Intrapartum transperineal ultrasonography for measuring AoD can be a valuable adjunct in labor management. Further investigations are needed to validate the optimal AoD in Indonesian pregnant women.

MEDIAN EPISIOTOMY APPROACH IN A MINIATURE POMERANIAN WITH VULVAL HYPOPLASIA: A CASE REPORT

This case report details the episiotomy approach employed in managing vulval hypoplasia in a two year old Miniature Pomeranian bitch. The animal was presented to the Teaching Veterinary Clinical Complex, Veterinary Gynaecology and Obstetrical Unit, Madras Veterinary College, displaying a history of greenish black discharge and continuous straining for two hours. The animal was bred before 64 days. Physical examination revealed a small vaginal opening, measuring approximately 1.5 cm in length. Digital examination indicated fetal hind limbs and tail extending into the vaginal passage, while all other vital parameters were within the normal range. Attempts to induce uterine contractions using Inj. Oxytocin and 10 % Calcium gluconate in 5 % DNS proved unsuccessful. Therefore, under local anesthesia (2 % Lignocaine injection), median episiotomy was made and a single live male puppy was successfully delivered with mild traction. Closure of the incision site was accomplished using PGA 1/0 with a simple interrupted suture pattern. The patient had uneventful recovery after 5 day treatment regimen, involving a single dose of subcutaneous Inj. Cefovecin (antibiotic) and anti-inflammatory drugs, along with meticulous wound management.

Prostate Cancer: A Review of Genetics, Current Biomarkers and Personalised Treatments.

Prostate cancer is the second leading cause of cancer deaths in men, second only to lung cancer. Despite this, diagnosis and prognosis methods remain limited, with effective treatments being few and far between. Traditionally, prostate cancer is initially tested for through a prostate serum antigen (PSA) test and a digital rectum examination (DRE), followed by confirmation through an invasive prostate biopsy. The DRE and biopsy are uncomfortable for the patient, so less invasive, accurate diagnostic tools are needed. Current diagnostic tools, along with genes that hold possible biomarker uses in diagnosis, prognosis and indications for personalised treatment plans, were reviewed in this article. Several genes from multiple families have been identified as possible biomarkers for disease, including those from the MYC and ETS families, as well as several tumour suppressor genes, Androgen Receptor signalling genes and DNA repair genes. There have also been advances in diagnostic tools, including MRI-targeted and liquid biopsies. Several personalised treatments have been developed over the years, including those that target metabolism-driven prostate cancer or those that target inflammation-driven cancer. Several advances have been made in prostate cancer diagnosis and treatment, but the disease still grows year by year, leading to more and more deaths annually. This calls for even more research into this disease, allowing for better diagnosis and treatment methods and a better chance of patient survival.

Data Synthesis Is Going Mobile—On Community-Driven Dataset Generation for Android Devices

Personal electronic devices such as smartphones and smartwatches have become indispensable daily companions, collecting a multitude of personal and sensitive data. As a result, they are of paramount importance in digital forensic examinations. However, there is a lack of publicly available and ready-to-use digital forensic datasets, especially in mobile forensics. This work presents a concept and an open-source proof-of-concept implementation, which simplifies and automates the creation of mobile forensic datasets within the scope of the Android operating system. In contrast to previous approaches, which populate the most common databases of an Android device, our concept is based on community-driven playbooks and makes use of interaction with the actual smartphone GUI. Hence, we are able to generate coherent and realistic traces as they occur in real-world human usage. Our proof-of-concept implementation is based on the standard Android emulation environment and borrows tools from the user interface testing community. Our evaluation shows that our approach actually generates realistic Android datasets. For instance, we can generate traces that cannot be simulated by gestures (e.g., changing the GPS position or triggering incoming phone calls). Recording the actual data synthesis process allows users to either create and share their own playbooks (i.e., the exact instructions for the data synthesis process rather than having to share the full image) or reproduce Android images with different scenarios using playbooks previously created and shared by the community.

Hotspots of Anal Cancer Screening in a High-risk Population: A Clinical Study on Free Provision, Best Method, Self-sampling, and Independent Risk Factors.

As of 2024, anal cancer (AC) has been steadily increasing worldwide but, due to insufficient evidence, anal cancer screening (ACS) has yet to be standardized. Furthermore, most high-risk people in the world have no help paying for it. Therefore, our primary endpoint was to assess the best screening method for these subjects through a provision that was free of charge (all costs were covered by the Italian public health service). Awareness-raising campaign, determination of risk factors, education on anal self-examination, and sampling (ASS) were secondary objectives. Screening was on a voluntary basis. Engaging in receptive anal intercourse and having a history of cervical dysplasia were the main inclusion criteria. Level 1 ACS tools included digital ano-rectal examination, anoscopy, anal Pap, and anal human papillomavirus (HPV) DNA test (both through self- and proctologist- sampling); high-resolution anoscopy (HRA) with (HRAB) or without biopsy comprised level 2 screening. High-risk people were enrolled until the available funds were exhausted. Fifty high-risk people (40 men who had sex with men -MSM-, 9 women, and 1 heterosexual man) were enrolled. AC was found in one HIV-seropositive MSM, high-grade squamous intraepithelial lesion in 10 (20%) MSM, low-grade squamous intraepithelial lesion LSIL in 13 cases (12 MSM and 1 woman). The combination of HRAB and Pap smear screening achieved the highest values for sensitivity, specificity, and accuracy. ASS HPV DNA test provided excellent results comparable to clinician retrieval. Overweight and college education were identified as independent factors for the risk of and prevention of AC, respectively. A free ACS not only appears justified but also recommended to people screened for AC. Excess weight represents a further risk for this population.

Causal Inconsistencies Are Normal in Windows Memory Dumps (Too)

Main memory contains valuable information for criminal investigations, e.g., process information or keys for disk encryption. Taking snapshots of memory is therefore common practice during a digital forensic examination. Inconsistencies in such memory dumps can, however, hamper their analysis. In this article, we perform a systematic assessment of causal inconsistencies in memory dumps taken on a Windows 10 machine using the kernel-level acquisition tool WinPmem. We use two approaches to measure the quantity of inconsistencies in Windows 10: (1) causal inconsistencies within self-injected memory data structures using a known methodology transferred from the Linux operating system, and (2) inconsistencies in the memory management data structures of the Windows kernel using a novel measurement technique based on properties of the virtual address descriptor (VAD) tree. Our evaluation is based on a dataset of more than 180 memory dumps. As a central result, both types of inconsistency measurement reveal that a high number of inconsistencies is the norm rather than the exception. We also correlate workload and execution time of the memory acquisition tool to the number of inconsistencies in the respective memory snapshot. By controlling these factors it is possible to (somewhat) control the level of inconsistencies in Windows memory dumps.

Early onset neonatal sepsis and its associatited factors: a cross sectional study

BackgroundSepsis is the 3rd leading cause of neonatal mortality in Ethiopia contributing to 16% of neonatal death. In a hospital study, neonatal sepsis was the leading diagnosis at admission and the second leading cause of neonatal death at the neonatal intensive care unit. Among other factors repeated vaginal examination during labor is known to contribute to sepsis in low-income settings. However, there is limited evidence in the Ethiopian setting.ObjectiveThe objective of this study was to examine the association between early-onset neonatal sepsis and repeated vaginal examinations.MethodsThe study was conducted at Gandhi Memorial Hospital, a public maternity and newborn care hospital. We followed 672 mother-newborn pairs by phone until 7 days of age to detect clinical sepsis. Data were analyzed using SPSS version 20 software. Adjusted odds ratio risk (AOR) with a corresponding 95% confidence interval (CI) was used to show the strength of associations and variables with p-value < 0.05 were considered to be statistically significant.ResultsThe incidence of early-onset neonatal sepsis was found to be 20.83% (95% CI 17.60, 24.00). Having a frequent vaginal examination (four or more times) during labor and delivery, prolonged rupture of membranes, induced labor and gestational age < 37 weeks were strongly associated with the development of early-onset neonatal sepsis, (AOR 2. 69;95 CI: 1.08, 6.70) AOR 5.12(95% CI 1.31, 20.00), AOR of 5.24 (95% CI 1.72, AOR4.34 (95% CI 1.20, 15.68), 16.00), respectively.ConclusionFrequent digital vaginal examination prolonged rupture of membranes, induced labor and gestational age < 37 weeks significantly increases the risk of early onset neonatal sepsis. We also recommend further study using neonatal blood culture to better diagnose early onset neonatal sepsis objectively.

Digital Breast Tomosynthesis Screening Improves Early Breast Cancer Detection and Survival in Taiwan.

Our objective was to compare the efficacy of digital breast tomosynthesis (DBT) and digital mammography (DM) in breast cancer screening and their impact on long-term overall survival (OS). The study involved 48 549 consecutive mammography examinations between 2011 and 2015 at a medical center in Taiwan, identifying 545 women who were screened and diagnosed with breast cancer. Digital mammography and DBT examinations were alternated on different days. Patients were categorized based on mammographic modality, breast density, and American Joint Committee on Cancer (AJCC) stage. To determine the long-term outcome until August 2021, survival rates were analyzed using the Kaplan-Meier (K-M) survival analysis. The mean age at breast cancer diagnosis was 53.2 years. Digital breast tomosynthesis examinations were significantly associated with early breast cancer (AJCC stage 0 to 2) (P = .022). The 5- and 9-year OS rates for all patients were 96.8% and 93.0%, respectively. The 5- and 9-year OS was significantly greater in the DBT group (98.4% and 96.8%) compared with the DM group (95.0% and 90.4%) (P = .030 for all). The K-M survival analysis demonstrated a significantly higher OS in the DBT group than the DM group (P = .037). Furthermore, DBT significantly improved OS in a cohort of women with stage II and III cancer (P = .032) and heterogeneously dense breasts (P = .045). Screening with DBT is associated with early breast cancer diagnosis and higher survival rates compared with DM.

Digital Otoscopy With Computer-Aided Composite Image Generation: Impact on the Correct Diagnosis, Confidence, and Time.

This study investigated the comparative performance of ear, nose, and throat (ENT) physicians in correctly detecting ear abnormalities when reviewing digital otoscopy imaging using 3 different visualization methods, including computer-assisted composite images called "SelectStitch," single video frame "Still" images, and video clips. The study also explored clinicians' diagnostic confidence levels and the time to make a diagnosis. Clinician diagnostic reader study. Online diagnostic survey of ENT physicians. Nine ENT physicians reviewed digital otoscopy examinations from 86 ears with various diagnoses (normal, perforation, retraction, middle ear effusion, tympanosclerosis). Otoscopy examinations used artificial-intelligence (AI)-based computer-aided composite image generation from a video clip (SelectStitch), manually selected best still frame from a video clip (Still), or the entire video clip. Statistical analyses included comparisons of ability to detect correct diagnosis, confidence levels, and diagnosis times. The ENT physicians' ability to detect ear abnormalities (33.2%-68.7%) varied depending on the pathologies. SelectStitch and Still images were not statistically different in detecting abnormalities (P > .50), but both were different from Video (P < .01). However, the performance improvement observed with Videos came at the cost of significantly longer time to determining the diagnosis. The level of confidence in the diagnosis was positively associated with correct diagnoses, but varied by particular pathology. This study explores the potential of computer-assisted techniques like SelectStitch in enhancing otoscopic diagnoses and time-saving, which could benefit telemedicine settings. Comparable performance between computer-generated and manually selected images suggests the potential of AI algorithms for otoscopy applications.

AIDA (Artificial Intelligence Dystocia Algorithm) in Prolonged Dystocic Labor: Focus on Asynclitism Degree.

Asynclitism, a misalignment of the fetal head with respect to the plane of passage through the birth canal, represents a significant obstetric challenge. High degrees of asynclitism are associated with labor dystocia, difficult operative delivery, and cesarean delivery. Despite its clinical relevance, the diagnosis of asynclitism and its influence on the outcome of labor remain matters of debate. This study analyzes the role of the degree of asynclitism (AD) in assessing labor progress and predicting labor outcome, focusing on its ability to predict intrapartum cesarean delivery (ICD) versus non-cesarean delivery. The study also aims to assess the performance of the AIDA (Artificial Intelligence Dystocia Algorithm) algorithm in integrating AD with other ultrasound parameters for predicting labor outcome. This retrospective study involved 135 full-term nulliparous patients with singleton fetuses in cephalic presentation undergoing neuraxial analgesia. Data were collected at three Italian hospitals between January 2014 and December 2020. In addition to routine digital vaginal examination, all patients underwent intrapartum ultrasound (IU) during protracted second stage of labor (greater than three hours). Four geometric parameters were measured using standard 3.5 MHz transabdominal ultrasound probes: head-to-symphysis distance (HSD), degree of asynclitism (AD), angle of progression (AoP), and midline angle (MLA). The AIDA algorithm, a machine learning-based decision support system, was used to classify patients into five classes (from 0 to 4) based on the values of the four geometric parameters and to predict labor outcome (ICD or non-ICD). Six machine learning algorithms were used: MLP (multi-layer perceptron), RF (random forest), SVM (support vector machine), XGBoost, LR (logistic regression), and DT (decision tree). Pearson's correlation was used to investigate the relationship between AD and the other parameters. A degree of asynclitism greater than 70 mm was found to be significantly associated with an increased rate of cesarean deliveries. Pearson's correlation analysis showed a weak to very weak correlation between AD and AoP (PC = 0.36, p < 0.001), AD and HSD (PC = 0.18, p < 0.05), and AD and MLA (PC = 0.14). The AIDA algorithm demonstrated high accuracy in predicting labor outcome, particularly for AIDA classes 0 and 4, with 100% agreement with physician-practiced labor outcome in two cases (RF and SVM algorithms) and slightly lower agreement with MLP. For AIDA class 3, the RF algorithm performed best, with an accuracy of 92%. AD, in combination with HSD, MLA, and AoP, plays a significant role in predicting labor dystocia and labor outcome. The AIDA algorithm, based on these four geometric parameters, has proven to be a promising decision support tool for predicting labor outcome and may help reduce the need for unnecessary cesarean deliveries, while improving maternal-fetal outcomes. Future studies with larger cohorts are needed to further validate these findings and refine the cut-off thresholds for AD and other parameters in the AIDA algorithm.

Comparing Pre-Induction Ultrasound Parameters and the Bishop Score to Determine Whether Labor Induction Is Successful.

Background and Objectives: The incidence of labor induction is steadily increasing worldwide. The main aim of this study was to evaluate the ultrasound parameters and their mutual correlation and to analyze the parameters' predictive capability in assessing the success of labor induction. The secondary goal was to assess patients' tolerability and acceptance of transvaginal ultrasound and digital gynecological examination. Materials and Methods: This prospective observational follow-up study included 252 women selected for labor induction. The transvaginal ultrasound examination measured the posterior cervical angle, cervical length, the length and width funneling of the cervix, the distance between the head of the fetus and the external uterine os, and the position of the fetal occiput. After the ultrasound, a digital vaginal examination was performed (according to the Bishop score), and the women were asked to rate their perception of pain for each procedure. Results: The most common indication for labor induction was post-term pregnancy (57.59%), and the most common method of labor induction was oxytocin with amniotomy (70%). The results showed that a significant independent prediction of vaginal delivery could be provided based on the Bishop score and cervical length. Other investigated ultrasound parameters, the length and width of the funneling of the cervix (p < 0.001), the fetal head stage (p < 0.001), and the size of the posterior cervical angle (p < 0.05), showed statistical significance in relation to the success of labor induction. Patients reported lower discomfort and pain during transvaginal ultrasound examination (mean score 2, IQR 3) compared to digital examination (mean score 5, IQR 4), with p < 0.001. Conclusions: The results imply that the assessment of ultrasound parameters before induction of labor is necessary to predict the outcome and reduce the possibility of complications. In terms of tolerability and choice by the patients, the transvaginal ultrasound examination was better rated than the vaginal gynecological examination.

Value of Transabdominal Sonographic Rectal Diameter in Children with Functional Constipation

Abstract Background Constipation is a common problem worldwide. Since rectal digital examination and abdominal radiography have both limitations, ultrasound is considered an alternative technique, thus the aim of the current study is measurement of rectal diameter among a group of children with functional constipation (FC) in comparison with a healthy control group to determine the diagnostic efficacy of dilated rectal diameter for diagnosis of constipation. Patients and Methods A case control study that was conducted on children aged between 2 and 10 years; 50 were cases with functional constipation who were recruited from gastroenterology department and an equal number of age and sex matched apparently normal and healthy children were included as a control. FC was diagnosed according to the ROME IV criteria. Pelviabdominal ultrasound was done to all cases and controls to assess the rectal transverse diameter after taking detailed history regarding personal history, symptoms and history of present illness, past history and family history. Results Rectal diameter was measured in cases initially on presentation where 80% of the cases had enlarged rectal diameter, and 20% of cases had normal rectal diameter, ranging from 1.6 cm to 4.7 cm with a median of 3.55, while only 8% of controls has enlarged rectal transverse diameter and others were normal (P value &amp;lt;0.001). Conclusion based on our results, it can be concluded that rectal diameter is. significantly enlarged in most cases with constipation.

A Generic Integrated Framework of Unsupervised Learning and Natural Language Processing Techniques for Digital Healthcare: A Comprehensive Review and Future Research Directions

Abstract:: The increasing availability of digital healthcare data has opened up fresh prospects for improving healthcare through data analysis. Machine learning (ML) procedures exhibit great promise in analyzing large volumes of healthcare data to extract insights that could be utilized to improve patient outcomes and healthcare delivery. In this work, we suggest an integrated framework for digital healthcare data analysis by integrating unsupervised learning techniques and natural language processing (NLP) techniques into the analysis pipeline. The module on unsupervised learning will involve techniques, such as clustering and anomaly detection. By clustering similar patients together based on their medical history and other relevant factors, healthcare providers can identify subgroups of patients who may require different treatment approaches. Anomaly detection can also help to detect patients who stray from the norm, which could be indicative of underlying health issues or other issues that need additional investigation. The second module on NLP will enable healthcare providers to analyze unstructured text data such as clinical notes, patient surveys, and social media posts. NLP techniques can help to identify key themes and patterns in these datasets, requiring awareness that could not be readily apparent through other means. Overall, incorporating unsupervised learning techniques and NLP into the analysis pipeline for digital healthcare data possesses the promise to enhance patient results and lead to more personalized treatments, and represents a potential domain for upcoming research in this field. In this research, we also review the current state of research in digital healthcare information examination with ML, including applications like forecasting clinic readmissions, finding cancerous tumors, and developing personalized drug dosing recommendations. We also examine the potential benefits and challenges of utilizing ML in healthcare data analysis, including issues related to data quality, privacy, and interpretability. Lastly, we discuss the forthcoming research paths, involving the necessity for enhanced methods for incorporating information from several resources, developing more interpretable ML patterns, and addressing ethical and regulatory challenges. The usage of ML in digital healthcare data analysis promises to transform healthcare by empowering more precise diagnoses, personalized treatments, and improved health outcomes, and this work offers a complete overview of the current trends.

Traditional Hands-On Puzzle Method versus Fully Digital Approach in Teaching Tooth Morphology: A Comparative Study of Educational Outcomes.

Tooth morphology education is a critical component of dental curricula, providing a foundational understanding of the intricate structural anatomy of teeth. This study evaluates the learning outcomes in relation to tooth morphology of two student cohorts from different academic terms, comparing the traditional 'tooth puzzle' method to an alternative fully digital approach. Two groups of Master of Dentistry students were retrospectively analyzed. The control group (55 students) was taught via the 'tooth puzzle' method in 2021, while the experimental group (55 students) underwent a fully digital course in 2020 due to COVID-19 restrictions. Both groups completed a digital examination involving the identification of 40 teeth, presented as images and videos. In the control group of 55 students, nearly half (49.1%) achieved faultless results, while 65.5% had at most two faults, and 74.5% had no more than four faults. The group had a total of 163 faults, averaging 3.0 per student, with only one student (1.8%) failing the test. In stark contrast, the experimental group had no students without faults, 9.1% had four or fewer faults, and a significant 61.8% made 10 or more faults, with 29.1% failing their first test attempt by exceeding 12 faults. Overall, the experimental group registered 582 faults, averaging 10.6 per student. The 'tooth puzzle' method, with its interactive and tactile elements, proved more effective in teaching tooth morphology than the digital-only approach. The increased number of faults and failed tests in the experimental group suggest that while digital tools offer meaningful support in learning tooth morphology, their main advantage is seen when coupled with traditional hands-on techniques, not unassisted and independently.

Combining RNAscope, Immunohistochemistry (IHC) and Digital Image Analysis to Assess Podoplanin (PDPN) Protein and PDPN_mRNA Expression on Formalin-Fixed Paraffin-Embedded Normal Human Placenta Tissues.

The expression and function of podoplanin (PDPN) in the normal human placenta has been debated in placental evaluation. This study emphasizes the importance of a multimodal approach of PDPN expression in normal human placentas. A complete examination is performed using immunohistochemistry, RNAscope and automated Digital Image examination (DIA) interpretation. QuPath DIA-based analysis automatically generated the stromal and histological scores of PDPN expression for immunohistochemistry and RNAscope stains. The umbilical cord's isolated fibroblasts and luminal structures expressed PDPN protein and PDPN_mRNA. RNAscope detected PDPN_mRNA upregulation in syncytial placental knots trophoblastic cells, but immunohistochemistry did not certify this at the protein level. The study found a significant correlation between the IHC and RNAscope H-Score (p = 0.033) and Allred Score (p = 0.05). A successful multimodal strategy for PDPN assessment in human placentas confirmed PDPN expression heterogeneity in the full-term human normal placenta and umbilical cord at the protein and mRNA level. In placental syncytial knots trophoblastic cells, PDPN showed mRNA overexpression, suggesting a potential role in placenta maturation.

Magnitude of neonatal sepsis and its associated factors among neonates admitted to the neonatal intensive care unit in Hawassa Comprehensive Specialized Hospital, Sidama Regional State, Ethiopia, 2020

BackgroundNeonatal sepsis is one of the most common causes of disease and death among neonates globally. And it made a great contribution to neonatal admission to intensive care units. To mitigate the ongoing neonatal crisis and accomplish the goal of sustainable development through a decrease in neonatal mortality, information from various regions is needed. Despite the considerable burden of neonatal sepsis in our setting, no prior studies were conducted in the study area. So, this study aimed to assess the magnitude and associated factors of neonatal sepsis among neonates admitted to the neonatal intensive care unit at Hawassa University Comprehensive Specialized Hospital, Sidama Regional State, Ethiopia.MethodsA hospital-based cross-sectional study was carried out among 287 neonates from March 1, 2020, to April 25, 2020. An interviewer-administered structured questionnaire was used to collect the data. The data were cleaned, coded, and entered into Epi Data 3.1 software and exported to Statistical Package for Social Science (SPSS) software version 23.0 for analysis. Binary logistic regression analyses were performed to identify variables having a significant association with neonatal sepsis. A p-value of ≤ 0.05 was considered statistically significant during multivariable logistic regression.ResultsThe study found that the magnitude of neonatal sepsis was 56%. The mean age of neonates was 3.2(SD±2.2) days. Around two-fifths (39%) of neonates were in the gestational age of <37 completed weeks. A quarter of mothers(25.8%) were delivered through cesarean section. During labor, 251 (87.5%) mothers had ≤4 digital vaginal examinations. Moreover, the finding revealed that mothers who delivered by cesarean section [AOR = 2.13, 95% CI (1.090-4.163)]. neonates who had been resuscitated at birth [AOR = 4.5, 95% CI (2.083-9.707)], and neonates who had NG tube inserted [AOR = 4.29, 95% CI (2.302-8.004)] were found to be significantly associated with neonatal sepsis.ConclusionsThe current study shows that neonatal sepsis was prevalent among more than half of the neonates admitted to the NICU. Therefore, designing strategies to enhance the aseptic techniques of professionals in the provision of care and actively and collaboratively working with cluster health facilities is highly recommended.

The Role of TLR-2 in Lethal COVID-19 Disease Involving Medullary and Resident Lung Megakaryocyte Up-Regulation in the Microthrombosis Mechanism.

The aim of this study was to study the expression of platelet glycoprotein IIIa (CD61), playing a critical role in platelet aggregation, together with TRL-2 as a marker of innate immune activation. A total of 25 patients were investigated, with the majority (24/25, 96%) having co-morbidities and dying from a fatal form of SARS-CoV-2(+) infection (COVID-19+), with 13 men and 12 females ranging in age from 45 to 80 years. When compared to a control group of SARS-CoV-2 (-) negative lungs (COVID-19-), TLR-2 expression was up-regulated in a subset of patients with deadly COVID-19 fatal lung illness. The proportion of Spike-1 (+) patients found by PCR and ISH correlates to the proportion of Spike-S1-positive cases as detected by digital pathology examination. Furthermore, CD61 expression was considerably higher in the lungs of deceased patients. In conclusion, we demonstrate that innate immune prolonged hyperactivation is related to platelet/megakaryocyte over-expression in the lung. Microthrombosis in deadly COVID-19+ lung disease is associated with an increase in the number of CD61+ platelets and megakaryocytes in the pulmonary interstitium, as well as their functional activation; this phenomenon is associated with increased expression of innate immunity TLR2+ cells, which binds the SARS-CoV-2 E protein, and significantly with the persistence of the Spike-S1 viral sequence.