Assessing an AI-based Smart Imagery Framing and Truthing (SIFT) system to assist radiologists annotating lung abnormalities on chest X-ray images for development of deep learning models

To assess a Smart Imagery Framing and Truthing (SIFT) system in automatically labeling and annotating chest X-ray (CXR) images with multiple diseases as an assist to radiologists on multi-disease CXRs. SIFT system was developed by integrating a convolutional neural network based-augmented MaskR-CNN and a multi-layer perceptron neural network. It is trained with images containing 307,415 ROIs representing 69 different abnormalities and 67,071 normal CXRs. SIFT automatically labels ROIs with a specific type of abnormality, annotates fine-grained boundary, gives confidence score, and recommends other possible types of abnormality. An independent set of 178 CXRs containing 272 ROIs depicting five different abnormalities including pulmonary tuberculosis, pulmonary nodule, pneumonia, COVID-19, and fibrogenesis was used to evaluate radiologists' performance based on three radiologists in a double-blinded study. The radiologist first manually annotated each ROI without SIFT. Two weeks later, the radiologist annotated the same ROIs with SIFT aid to generate final results. Evaluation of consistency, efficiency and accuracy for radiologists with and without SIFT was conducted. After using SIFT, radiologists accept 93% SIFT annotated area, and variation across annotated area reduce by 28.23%. Inter-observer variation improves by 25.27% on averaged IOU. The consensus true positive rate increases by 5.00% (p=0.16), and false positive rate decreases by 27.70% (p<0.001). The radiologist's time to annotate these cases decreases by 42.30%. Performance in labelling abnormalities statistically remains the same. Independent observer study showed that SIFT is a promising step toward improving the consistency and efficiency of annotation, which is important for improving clinical X-ray diagnostic and monitoring efficiency. © 2023 SPIE.

The epidemiology of birth defects

Birth defects are emerging as the one of the leading causes of infant death worldwide. Their epidemiological investigation was prompted by the recognition of congenital rubella syndrome and of thalidomide-related phocomelia. Pediatric surgeons require sound data on birth defects as a baseline for reporting their own outcomes. Hence, birth defects data are the foundation for quality control and improvement in neonatal surgery. However, meaningful epidemiological studies of birth defects are often challenged practically by limited resources and dispersed populations, as well as scientifically by prioritization of reductionist genetic investigations. Instead, it may be more helpful to see birth defects as complex systems problems, similar to surgical errors. Accordingly, a better understanding of birth defects may require pediatric surgeons equipped with training in statistics, modeling and complex dynamic systems, rather than the current popularity for molecular biology approaches. Finally, birth defects are sensitive to widely different influences ranging from assisted reproduction technology to climate change. Thus, for a greater number of population health issues, birth defects may provide an early warning signal that can only be tracked with appropriate epidemiological measurements. The COVID-19 pandemic only emphasizes this need for investment in public health and the science of populations. © Springer Nature Switzerland AG 2023. All rights reseverd.

Macerated stillbirth

Stillbirth is defined as the birth of a viable baby without signs of life. They account for more than 2.5 million intrauterine deaths per year worldwide and are associated with a number of risk factors, the most important of which are maternal and placental factors. Autopsy provides information that may be of use in determining time since death, gestational age of the fetus, mode of death, cause of fetal demise, and the likelihood of recurrence. The format of the autopsy is guided by parental consent, but even when consent is limited, valuable information may be obtained by careful consideration of antemortem test results, imaging, and genetic testing. Where there is a delay between death and delivery, fetuses are affected by maceration, which may increase the technical complexity of the autopsy and impart a number of artefactual changes, which should not be misinterpreted as genuine pathology. The most common pathologies encountered at autopsy are placental abnormalities, changes related to maternal disorders, malformations, and central nervous system pathology. © Springer Nature Switzerland AG 2022. All rights reserved.

'My biggest fear is that people will forget about him': Mothers' emotional transitions after terminating their pregnancy for medical reasons.

AIMS AND OBJECTIVES: To explore women's emotional responses throughout the process of terminating a pregnancy for medical reasons. BACKGROUND: Making the choice to terminate a desired pregnancy for medical reasons has a negative impact on women's health, as it is a distressing process that involves making hard decisions and readjusting one's expectations of an idealised pregnancy. METHODS: A qualitative phenomenological study was conducted following the COREQ checklist. Fifteen semi-structured interviews and two focus groups were conducted with women who had terminated their pregnancies for medical reasons, previous to and during the COVID-19 lockdown. Subsequently, we analysed the content. RESULTS: One main category, emotional journey during the process of terminating the pregnancy, and six subcategories were identified: (I) representation and desire to become a mother, (II) main concerns, (III) impact of the news, (IV) decision-making, (V) emotional responses before termination for medical reasons and (VI) emotional responses after termination for medical reasons. All contributed to understanding the specificities of the different phases that make up the emotional journey of terminating a pregnancy for medical reasons. CONCLUSIONS: The findings of this study suggest that there are a number of predominant emotions that professionals need to be aware of in order to help women work through them and lessen the impact of pregnancy termination on their mental health. COVID-19 had different connotations depending on the women's experiences. RELEVANCE TO CLINICAL PRACTICE: Our results highlight how important the role of healthcare staff is in caring for these women and their partners, which involves recognising their emotions throughout the process. Our results also underline how useful it is to conduct qualitative studies in this context, since they constitute a set of activities and interventions that result in the administration of nursing care in itself. PATIENT OR PUBLIC CONTRIBUTION: The ultimate goal of the action research study is to design a positive mental health intervention. Participants will contribute to the design and final approval of the intervention.

Evaluation of menstrual symptoms after Coronavirus disease 2019 vaccination in women with endometriosis.

BACKGROUND: Vaccination can have an impact on menstruation, and this impact may be more notable in women with inflammatory gynecological pathologies such as endometriosis. OBJECTIVES: We aimed to investigate the impact of mRNA-based SARS-CoV-2 vaccines on menstrual cycle-related symptoms in women with endometriosis and assess the effect of hormonal therapy on potential SARS-CoV-2 vaccination-induced menstrual changes. DESIGN: A total of 848 women who received at least two doses of mRNA-based COVID-19 vaccines were prospectively recruited: 407 with endometriosis (endometriosis group) and 441 healthy controls (non-endometriosis group). METHODS: Data regarding demographics, clinical characteristics, hormonal treatment, and menstrual-associated symptoms in the first and second cycle after vaccination were collected through an online survey. RESULTS: A similar percentage of patients in both the endometriosis and the non-endometriosis group self-reported menstrual-associated changes the first (52.6% versus 48.8%, respectively) and second cycle after vaccination (29.0% versus 28.1%, respectively). Although the total symptoms recorded were not different between the two groups, several specific symptoms were statistically more frequent in the endometriosis group. These were pain disorders and fatigue in the first cycle after vaccination and pain disorders, menstrual headache and fatigue in the second cycle after vaccination. Bleeding frequency/regularity disorders were found to be more frequent in the non-endometriosis group in the first cycle after vaccination. Patients under hormonal treatment reported fewer changes in menstrual symptoms in the first and second cycle after vaccination compared with those not receiving this treatment. Similarly, patients in the endometriosis group receiving hormonal treatment reported fewer changes in menstrual-associated symptoms compared with those not following any hormonal treatment in the first and second menstrual cycle after the last vaccination. CONCLUSION: Women with endometriosis immunized with mRNA-based SARS-CoV-2 vaccines did not perceive greater worsening or new menstrual-associated symptoms after complete COVID-19 vaccination compared with healthy controls. Hormonal treatment may have a protective effect against worsened or new menstrual symptoms induced by COVID-19 vaccination.

Menstrual abnormalities after COVID-19 vaccination in the Netherlands: A description of spontaneous and longitudinal patient-reported data.

AIMS: During the COVID-19 vaccination campaigns, the number of reports of menstrual abnormalities increased rapidly. Here, we describe the nature and potential risk factors associated with menstrual abnormalities based on spontaneously reporting data as well as data from a prospective cohort event monitoring (CEM) study as these are poorly studied. METHODS: Reports of menstrual abnormalities received by the Netherlands Pharmacovigilance Centre Lareb in the spontaneous reporting system between February 2021 and April 2022 were summarized. In addition, logistic regression analysis was performed on the reported menstrual abnormalities in the CEM study to assess the association between person characteristics, prior SARS-CoV-2 infection and use of hormonal contraceptives and the occurrence of menstrual abnormalities after vaccination. RESULTS: We analysed over 24 000 spontaneous reports of menstrual abnormalities and over 500 episodes (among 16 929 included women) of menstrual abnormalities in the CEM study. The CEM study showed an incidence of 41.4 per 1000 women aged ≤54 years. Amenorrhoea/oligomenorrhoea and heavy menstrual bleeding collectively accounted for about half of all abnormalities reported. Significant associations were observed for the age group 25-34 years (odds ratio 2.18; 95% confidence interval 1.45-3.41) and the Pfizer vaccine (odds ratio 3.04; 95% confidence interval 2.36-3.93). No association was observed for body mass index and presence of most comorbidities assessed. CONCLUSION: The cohort study showed a high incidence of menstrual disorders among women aged ≤54 years, and this observation was supported by the analysis of spontaneous reports. This suggests that a relation between COVID-19 vaccination and menstrual abnormalities is plausible and should be further investigated.

Cardiopulmonary testing in long COVID-19 versus non-COVID-19 patients with undifferentiated Dyspnea on exertion.

BACKGROUND: Dyspnea and fatigue are characteristics of long SARS-CoV-2 (COVID)-19. Cardiopulmonary exercise testing (CPET) can be used to better evaluate such patients. RESEARCH QUESTION: How significantly and by what mechanisms is exercise capacity impaired in patients with long COVID who are coming to a specialized clinic for evaluation? STUDY DESIGN AND METHODS: We performed a cohort study using the Mayo Clinic exercise testing database. Subjects included consecutive long COVID patients without prior history of heart or lung disease sent from the Post-COVID Care Clinic for CPET. They were compared to a historical group of non-COVID patients with undifferentiated dyspnea also without known cardiac or pulmonary disease. Statistical comparisons were performed by t-test or Pearson's chi2 test controlling for age, sex, and beta blocker use where appropriate. RESULTS: We found 77 patients with long COVID and 766 control patients. Long COVID patients were younger (47 ± 15 vs 50 ± 10 years, P < .01) and more likely female (70% vs 58%, P < .01). The most prominent difference on CPETs was lower percent predicted peak V̇O2 (73 ± 18 vs 85 ± 23%, p < .0001). Autonomic abnormalities (resting tachycardia, CNS changes, low systolic blood pressure) were seen during CPET more commonly in long COVID patients (34 vs 23%, P < .04), while mild pulmonary abnormalities (mild desaturation, limited breathing reserve, elevated V̇E/V̇CO2) during CPET were similar (19% in both groups) with only 1 long COVID patient showing severe impairment. INTERPRETATION: We identified severe exercise limitation among long COVID patients. Young women may be at higher risk for these complications. Though mild pulmonary and autonomic impairment were common in long COVID patients, marked limitations were uncommon. We hope our observations help to untangle the physiologic abnormalities responsible for the symptomatology of long COVID.

Inclusive Community Aquatics Programming for Children with Developmental Challenges: A Community Participatory Action Research

Background: Children who are neurodiverse have traditionally been segregated from their peers in community-based programs, despite evidence of health benefits of inclusive education. Objectives: This community-initiated project aims to explore barriers and facilitators to inclusive aquatics programming for children with developmental and/or mental health challenges. Methods: Using a participatory-action research methodology, semi-structured interviews and focus groups were conducted with 14 participants from various stakeholder groups, including parents of children who are neurodiverse, helping professionals, and community programmers. Results: Participants described unique definitions of inclusion, from integration with neurotypical peers, to individualized goal-setting and achievement. Major facilitators include adequate resources, flexibility around accommodations, and motivated staff. Major barriers include social stigma, financial limitations, and lack of communication between caregivers and service providers. Conclusions: Participants felt strongly about the need to improve inclusion practices within aquatics—and other community-based—programs. Increased collaboration between families, community programmers, and helping professionals can foster better inclusion and outcomes for children who are neurodiverse. By incorporating various perspectives into the design of future programs, program administrators can ensure more equitable access such that all children are able to participate.

Focus on laryngeal disease in the cat

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Evaluation of renal function when using dexamethasone therapy in Patients with Covid-19 in Najaf City

Background: The COVID-19 is the one of the most reason that induce the renal problems and could even attack so many organs that might ends with increase the mortality and morbidity rate in the worldwide. One of its common cases is the chronic renal impairment that may be worse and increase the death rate in complete impair renal function. On the other hand the treatment or restoring of the renal abnormality could be occurred in hospital monitoring with a steroid treatment. A regular steroid intake with assessment of renal function base on a urea, creatinine and glucose levels is a good road path to improve the renal function and so shorten the time required for the patient to stay in the hospital. Aim: to predict if the steroid treatment (dexamethasone) could improve the renal function and facilitate the curing rate of the patients. Materials and Method: a case control study was conducted from sixty individuals, one half take the dexamethasone and suffer from COVID with the renal impairment and the other thirty patient was also have the COVID and renal abnormality but without this steroid drug, the blood sample and serum glucose, creatinine and urea measurement was performed. Results: data showed that the level of urea, creatinine and glucose concentrations is lower in those COVID patient with renal abnormalities and take a steroid treatment compared to those didn't take the course of this drug. Conclusion: steroid treatment is one of the drugs that might improve the case of the patient and might participate in faster the curing rate of the patient and resorting the renal function to the normal level.

evaluation of hematological abnormalities in subjects vaccinated with bnt162b2 in a longitudinal cohort of the coRe study

Background: Vaccination is considered the most effective preventive strategy to fight COVID-19. The aim of this study was to evaluate two critical concerns about: 1) the kinetic response of IgG and IgM, and: 2) the hematological abnormalities in a longitudinal cohort of health-care workers (HCW) who had received 2 doses of BNT162b2 mRNA-based vaccine. Method(s): Blood and nasopharyngeal swabs were collected from 46 volunteers' participants, previous written consensus, with presumable no symptoms of COVID-19. Anti-SARS-CoV-2 serum immunoglobulin G (IgG) and M (IgM) and hematological parameters were examined. Multivariable mixed-effects models for repeated measure analysis were adopted to evaluate time changes in IgG, IgM and hematological parameters, and to investigate associations with vaccination response. Result(s): Forty-six subjects (N.=46;31.8% men;68.2% women;mean age near 36 years-old) were enrolled among healthcare workers of IRCCS MultiMedica (Milan, Italy). Overall, increase in serological IgG concentration appeared mainly between 21-28 days after the 1st dose, whereas IgM did not reach positivity in all cases. Mean blood cells counts were in normal range but we observed a significant reduction of total white blood cells and absolute lymphocyte counts after the 1st dose, persisting until the day 28. The increase of monocytes and neutrophils the day after the 1st dose subsequently decayed significantly. Eosinophils concentration showed a tendency to increase over time. Peripheral blood smear showed a growing frequency of atypical lymphocytes (lympho-variants), and of plasmacytoid forms, whereas no difference was found in large granular lymphocytes (LGL), although a decay after the boost was evident. The stratification of subjects, relative to the timing of IgG increase, showed the occurrence of 3 different patterns after vaccination, namely early-responders (R+), late-responders (R-) and pauci-responders (PR) with a peculiar kinetics of hematological parameters. Lymphocytes were significantly associated with total IgG: lower in R+ and PR compared to R- (P=0.0193 and P=00054, respectively). Conclusion(s): In healthy subjects, anti SARS-CoV-2 vaccination induced a variety of non-pathologic abnormalities. The response to vaccination was not equal in the groups examined. In PR group a major difference occurred with respect to R- and R+. This work adds novel insight into the puzzle of changes induced by SARS-CoV-2 virus.Copyright © 2022 EDIZIONI MINERVA MEDICA.

Pulmonary circulation abnormalities in post-acute COVID-19 syndrome: dual-energy CT angiographic findings in 79 patients.

OBJECTIVES: To evaluate the frequency and pattern of pulmonary vascular abnormalities in the year following COVID-19. METHODS: The study population included 79 patients remaining symptomatic more than 6 months after hospitalization for SARS-CoV-2 pneumonia who had been evaluated with dual-energy CT angiography. RESULTS: Morphologic images showed CT features of (a) acute (2/79; 2.5%) and focal chronic (4/79; 5%) PE; and (b) residual post COVID-19 lung infiltration (67/79; 85%). Lung perfusion was abnormal in 69 patients (87.4%). Perfusion abnormalities included (a) perfusion defects of 3 types: patchy defects (n = 60; 76%); areas of non-systematized hypoperfusion (n = 27; 34.2%); and/or PE-type defects (n = 14; 17.7%) seen with (2/14) and without (12/14) endoluminal filling defects; and (b) areas of increased perfusion in 59 patients (74.9%), superimposed on ground-glass opacities (58/59) and vascular tree-in-bud (5/59). PFTs were available in 10 patients with normal perfusion and in 55 patients with abnormal perfusion. The mean values of functional variables did not differ between the two subgroups with a trend toward lower DLCO in patients with abnormal perfusion (74.8 ± 16.7% vs 85.0 ± 8.1). CONCLUSION: Delayed follow-up showed CT features of acute and chronic PE but also two types of perfusion abnormalities suggestive of persistent hypercoagulability as well as unresolved/sequelae of microangiopathy. CLINICAL RELEVANCE STATEMENT: Despite dramatic resolution of lung abnormalities seen during the acute phase of the disease, acute pulmonary embolism and alterations at the level of lung microcirculation can be identified in patients remaining symptomatic in the year following COVID-19. KEY POINTS: • This study demonstrates newly developed proximal acute PE/thrombosis in the year following SARS-CoV-2 pneumonia. • Dual-energy CT lung perfusion identified perfusion defects and areas of increased iodine uptake abnormalities, suggestive of unresolved damage to lung microcirculation. • This study suggests a complementarity between HRCT and spectral imaging for proper understanding of post COVID-19 lung sequelae.

A Case of Pure Pericarditis Associated With COVID-19: Application of Classical Clinical Evaluation for Differential Diagnosis.

Pericarditis of varying severity is being recognized as a rare complication of the COVID-19 infection. We present a patient with an electrocardiogram (EKG) and physical exam findings that initially seemed to most likely be pericarditis related to the COVID-19 infection. The differential diagnosis was a bit difficult because it included ST-segment elevation myocardial infarction (STEMI) due to some EKG changes and early repolarization changes that were rather robust. Treatment options for STEMI could cause severe harm if the process turned out to be pericarditis. Treatment options for pericarditis could cause severe harm if the process turned out to be STEMI. And treatment options for early repolarization might be no treatment at all, which could cause harm if the process turned out to be STEMI or pericarditis. In this case, a correct diagnosis was very important to ensure a good clinical outcome. We would like to share our thought processes in the management of this case.

A Lightweight CNN Architecture for Chest X-ray Images Analysis to Facilitate Early Stage Detection of Lung Diseases

Lung-related diseases are one of the significant causes of death among infants and children. However, the mortality rate can be reduced by the detection of lung abnormality at an early stage. Traditionally, radiologists identify irregularities by interpreting chest x-ray images which is time-consuming. Therefore, researchers have proposed many automated systems for diagnosing pneumonia and other lung-related diseases. Due to the remarkable performance of Convolutional Neural Networks(CNN) in image classification, it has gained immense popularity in chest x-ray image analysis. Most of the research has utilized famous pre-trained Imagenet models for more accurate analysis of Chest X-ray images. However, the problem with these architectures is that they have many parameters that increase the training time, which makes the detection process lengthy. This paper introduces a lightweight, compact, and well-tuned CNN architecture with far fewer parameters than the pre-trained model to analyze two of the most common lung diseases, pneumonia and Covid-19. We have evaluated our model on two benchmark datasets. Experimental results show that our lightweight CNN model has far fewer hyperparameters than other state-of-the-art models but achieves similar results. We have achieved an accuracy of 90.38% on the kermany dataset and 96.90% on the Covid-19 Radiography dataset. © 2022 IEEE.

Deep Learning Based Automated Chest X-ray Abnormalities Detection

Abnormalities related to the chest are a fairly common occurrence in infants as well as adults. The process of identifying these abnormalities is relatively easy but the task of actually classifying them into specific labels pertaining to specific diseases is a much harder endeavour. COVID-19 sufferers are multiplying at an exponential rate, putting pressure on healthcare systems all around the world. Because of the limited number of testing kits available, it is impractical to test every patient with a respiratory ailment using traditional methods. Thus in such dire circumstances, we propose the use of modern deep learning techniques to help in the detection and classification of a number of different thoracic abnormalities from a chest radiograph. The goal is to be able to automatically identify and localize multiple points of interest in a provided chest X-ray and act as a second level of certainty after the radiologists. On our publically available chest radiograph dataset, our methods resulted in a mean average precision of 0.246 for the detection of 14 different thoracic abnormalities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analyzing Chest X-Ray to Detect the Evidence of Lung Abnormality Due to Infectious Disease

Analyzing chest X-ray is the must especially when are required to deal of infectious disease outbreak, and COVID-19. The COVID-19 pandemic has had a large effect on almost every facet of life. As COVID-19 was a disease only discovered in recent history, there is comparatively little data on the disease, how it is detected, and how it is cured. Deep learning is a powerful tool that can be used to learn to classify information in ways that humans might not be able to. This allows computers to learn on relatively little data and provide exceptional results. This paper proposes a customized convolutional neural network (CNN) for the detection of COVID-19 from chest X-rays called basicConv. This network consists of five sets of convolution and pooling layers, a flatten layer, and two dense layers with a total of approximately 9 million parameters. This network achieves an accuracy of 95.8%, which is comparable to other high-performing image classification networks. This provides a promising launching point for future research and developing a network that achieves an accuracy higher than that of the leading classification networks. It also demonstrates the incredible power of convolution. This paper is an extension of a 2022 Honors Thesis (Henderson, Joshua Elliot, "Convolutional Neural Network for COVID-19 Detection in Chest X-Rays” (2022). Honors Thesis. 254. https://red.library.usd.edu/honors-thesis/254 ). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

SARS-CoV-2, fertility-related autoantibodies and reproductive injury

As the critical risk of the short-term SARS-CoV-2 infection is severe lung inflammation with a hypercoagulative state that could escalate to multiorgan failure, many possible long-term implications are still under investigation. This article will review the most recent findings related to the potential contribution of SARS-CoV-2 to male infertility. Viral-induced male infertility has been studied widely and in much detail in the past. Numerous viral infections have a well-known ability to induce orchitis, resulting in impaired testicular functions and male infertility. SARS-CoV-2 may be an additional virus that is related to male infertility for several reasons that will be dealt with in this review: [1] the strong affinity of SARS-CoV-2 for the human ACE2 receptor, [2] SARS-CoV-2 induced sex steroid hormonal abnormalities, [3] increased levels of oxidative stress in COVID-19, and [4] molecular mimicry between humans and components of SARS-CoV-2 leading to antigenic cross-reactivity phenomena. As these mechanisms might be responsible for SARS-CoV-2-induced male infertility, new evidence demonstrates testicular damage and semen abnormalities following the viral infection. Pathological findings of patients who died of COVID-19 exhibit injury to Sertoli cells and seminiferous tubules with a reduction in Leydig cells, which all are critical components of spermatogenesis. Moreover, impairment of sperm quality was found in SARS-CoV-2-infected patients. Those manifestations found in COVID-19-ill patients, parallel to sex steroid hormonal abnormalities, might critically influence spermatogenesis and SARS-CoV-2-induced male infertility. © 2023 Elsevier Inc. All rights reserved.

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

Kidney abnormality is one of the major concerns in modern society, and it affects millions of people around the world. To diagnose different abnormalities in human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, is used, which creates cross-sectional slices of the kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification and segmentation purposes. However, it has been difficult for clinicians to interpret the model's specific decisions and, thus, creating a "black box” system. Additionally, it has been difficult to integrate complex deep-learning models for internet-of-medical-things devices due to demanding training parameters and memory-resource cost. To overcome these issues, this study proposed (1) a lightweight customized convolutional neural network to detect kidney cysts, stones, and tumors and (2) understandable AI Shapely values based on the Shapley additive explanation and predictive results based on the local interpretable model-agnostic explanations to illustrate the deep-learning model. The proposed CNN model performed better than other state-of-the-art methods and obtained an accuracy of 99.52 ± 0.84% for K = 10-fold of stratified sampling. With improved results and better interpretive power, the proposed work provides clinicians with conclusive and understandable results.

Brachycephalic obstructive airway syndrome: an overview

One of the conformational issues by the explosion Of pet ownership throughout the COVID 19 pandemic is the Ming number dunes of brachycephalic obstructive envay syndrome (BOAS), which is a condition prevalent in some of the UK's most copular dog breeds The challenges the veterinary profession is not only to identify and treat affected individuals from within the population of dogs presenting to primary care clinicians, using surgical and non-surgical options but also to educate clients on how to recognise clinical signs of the disease as early as possible.

Medications safety for special population case studies: Pediatrics

Medication errors are among the most common medical errors, and studies have shown that the pediatric population is particularly vulnerable. Errors can occur at any stage of the medication process. We tried to build various cases, which highlighted different aspects of drug safety in pediatrics. The case studies focused on vancomycin infusion, supportive treatment in COVID-19-related multisystem inflammatory illness, side effect of antitubercular treatment drugs, management of respiratory failure, low cardiac functioning, acyclovir nephrotoxicity, stress ulcer, cyclophosphamide-induced hemorrhagic cystitis in rhabdomyosarcoma, blood pressure after aortic coarctation elective surgery, and use of paracetamol instead of NSAIDs in pediatrics. These cases would be useful in both as a diagnostic tool and as a way of monitoring certain conditions. © 2023 Elsevier Inc. All rights reserved.