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Medically hospitalized individuals have high rates of comorbid psychiatric, substance abuse, and behavioral disorders. Disruptive and sometimes aggressive behaviors may arise when mental health needs of patients go unrecognized or are inadequately addressed. Health care workers experience the most workplace violence compared with other professions, with nurses and nursing aides at highest risk. A Behavioral Emergency Support Team (BEST) model can be an effective approach to providing a customized response to a patient's agitation through identification of underlying clinical and environmental contributors to the onset of aggression as well as to provide behavioral education and support of nursing staff. Results from 2 years of BEST model use resulted in 124 events among 96 patients of whom 19 had repeated events. The most common reasons for codes were aggression (79%) and elopement threat/attempt (45%), and the most frequent patient diagnosis was cognitive impairment (54%). Development of a BEST model provides support to nurses that is not otherwise available for events that are disruptive to care in inpatient medical settings and help minimize the occurrence of workplace violence.

s Background Patient-facing health care workers (HCW) experience higher rates of COVID-19 infection, particularly at the start of the COVID-19 pandemic. However, rates of COVID-19 among front-line home health and hospice clinicians are relatively unknown. Methods Visit data from a home health care and hospice agency in New Jersey early in the pandemic was analyzed to examine COVID-19 infection rates separately for clinicians exposed to COVID-19-contagious patients, and those without exposure to known COVID-19 contagious patients. Results Between March 5 and May 31, 2020, among home health clinicians providing in-person care, clinicians treating at least one COVID-19 contagious patient had a case rate of 0.8% compared to 15.7% for clinicians with no exposure to known COVID-19 contagious patients. Among hospice clinicians providing in-person care, those who treated at least one COVID-19 contagious patient had a case rate of 6.5%, compared to 12.9% for clinicians with no known exposure to COVID-19 contagious patients. Non-White clinicians had a higher COVID-19 case rate than White clinicians (10.9% vs 6.2%) Discussion Lower rates of COVID-19 infection among clinicians providing care to COVID-19-contagious patients may result from greater attentiveness to infection control protocols and greater precautions in clinicians’ personal lives. Greater exposure to COVID-19-contagious patients prior to patient diagnosis (“unknown exposures”) may explain differences in infection rates between home health and hospice clinicians with workplace exposures. Conclusion Clinicians providing in-person care to COVID-19-contagious patients experience lower rates of COVID-19 infection than clinicians providing face-to-face care with no known exposure to COVID-19 contagious patients. Our findings suggest there was a low incidence of potential workplace infections

We are heading towards a digital era and have to brace ourselves to adapt to technological innovations. The interest in competencies, knowledge processing and measuring specific learning is accelerating throughout the world. Artificial intelligence (AI) is rapidly impacting the delivery of healthcare, medical education and dentistry is no exception. In dentistry, the tremendous amount of patient data requires intelligent softwares for computation and AI can be utilized for patient diagnosis, treatment planning, education and research. The speed at which new AI technologies are developing and introduced into clinical practice, warrants equipping our graduates with necessary knowledge and skills to exploit AI technologies to maximum benefit. Dentists are always at the front foot of embracing a new technology. Hence, understanding the various concepts and the techniques involved will have a clear advantage in the future. This review is an attempt to provide a perspective on the benefits of AI in dentistry and also emphasizes on the need to bring dental education reforms to foster competency based education model and Artifical intelligence.

According to the World Health Organization (WHO), patient Length of Stay (LOS) in hospitals is an important performance measurement and monitoring indicator. Prolonged LOS in the Intensive Care Unit (ICU) may lead to consuming hospital resources, manpower, and equipment. Therefore, accurate prediction of patient LOS may aid the healthcare specialists to take medical decisions and allocate medical team and resources. As well, the patient and insurance companies may use this prediction to manage their budget. In this paper, a framework for predicting patient LOS in the ICU using different machine learning (ML) techniques is proposed. Unlike most of the previous studies, this study relies on general medical features collected on patient admission regardless of the patient diagnosis. This provide a broad scope and cover all patients making this approach general and easy to use. The prediction accuracy of the proposed approach was recorded to be very high and different for each ML technique. For example, the best prediction accuracy was achieved by fuzzy with accuracy reach 92%, while classification tree managed to achieve a prediction accuracy of 90% coming in the second place.

OBJECTIVE: To characterize healthcare and behavioral service providers’ transportation-related discussions with their autistic and non-autistic patients. METHOD: 78 providers completed a cross-sectional survey assessing their transportation discussions with patients. We used Mann–Whitney U tests and chi-square tests to compare differences in provider reports by patient diagnosis. RESULTS: Compared with one in two providers who reported they discuss transportation with non-autistic patients, only one in five have these conversations with their autistic patients. Few (8%) providers felt prepared to assess driving readiness in autistic patients, yet only a quarter refer patients elsewhere. CONCLUSION: There is a critical need to develop resources for use in medical settings to effectively support autistic adolescents’ independence and mobility as they transition into adulthood.

Left bundle branch block (LBBB) is not just a simple electrocardiogram alteration. The intricacies of this general terminology go beyond simple conduction block. This review puts together current knowledge on the historical concept of LBBB, clinical significance, and recent insights into the pathophysiology of human LBBB. LBBB is an entity that affects patient diagnosis (primary conduction disease, secondary to underlying pathology or iatrogenic), treatment (cardiac resynchronization therapy or conduction system pacing for heart failure), and prognosis. Recruiting the left bundle branch with conduction system pacing depends on the complex interaction between anatomy, site of pathophysiology, and delivery tools.

Novel diagnostic and therapeutic radiopharmaceuticals are increasingly becoming a central part of personalized medicine. Continued innovation in the development of new radiopharmaceuticals is key to sustained growth and advancement of precision medicine. Artificial intelligence (AI) has been used in multiple fields of medicine to develop and validate better tools for patient diagnosis and therapy, including in radiopharmaceutical design. In this review, we first discuss common in silico approaches and focus on their utility and challenges in radiopharmaceutical development. Next, we discuss the practical applications of in silico modeling in design of radiopharmaceuticals in various diseases.

Background The 10th and 9th revisions of the International Statistical Classification of Diseases and Related Health Problems (ICD10 and ICD9) have been adopted worldwide as a well-recognized norm to share codes for diseases, signs and symptoms, abnormal findings, etc. The international Consortium for Clinical Characterization of COVID-19 by EHR (4CE) website stores diagnosis COVID-19 disease data using ICD10 and ICD9 codes. However, the ICD systems are difficult to decode due to their many shortcomings, which can be addressed using ontology. Methods An ICD ontology (ICDO) was developed to logically and scientifically represent ICD terms and their relations among different ICD terms. ICDO is also aligned with the Basic Formal Ontology (BFO) and reuses terms from existing ontologies. As a use case, the ICD10 and ICD9 diagnosis data from the 4CE website were extracted, mapped to ICDO, and analyzed using ICDO. Results We have developed the ICDO to ontologize the ICD terms and relations. Different from existing disease ontologies, all ICD diseases in ICDO are defined as disease processes to describe their occurrence with other properties. The ICDO decomposes each disease term into different components, including anatomic entities, process profiles, etiological causes, output phenotype, etc. Over 900 ICD terms have been represented in ICDO. Many ICDO terms are presented in both English and Chinese. The ICD10/ICD9-based diagnosis data of over 27,000 COVID-19 patients from 5 countries were extracted from the 4CE. A total of 917 COVID-19-related disease codes, each of which were associated with 1 or more cases in the 4CE dataset, were mapped to ICDO and further analyzed using the ICDO logical annotations. Our study showed that COVID-19 targeted multiple systems and organs such as the lung, heart, and kidney. Different acute and chronic kidney phenotypes were identified. Some kidney diseases appeared to result from other diseases, such as diabetes. Some of the findings could only be easily found using ICDO instead of ICD9/10. Conclusions ICDO was developed to ontologize ICD10/10 codes and applied to study COVID-19 patient diagnosis data. Our findings showed that ICDO provides a semantic platform for more accurate detection of disease profiles.

Although product family design methods are well established, little research has focused on Product-Service-System (PSS) family design. A PSS family design method is proposed in this paper that parallels methodology for designing product families. Separate platforms are proposed for products and for services. However, couplings between product and service platforms are identified and incorporated into the design method. Design problem formulations are proposed for PSS family platforms and for the PSS family itself, using a module-based approach, in contrast to a platform scaling approach. Alternative methods are investigated and compared for solving these problems. The application domain of Assistive Mobility (AM) is identified as a promising PSS family in this work. If smart technologies are integrated into AM devices, such as manual wheelchairs, powered wheelchairs, walkers, and rollators, then patient diagnosis and treatment, as well as device maintenance, services are enabled with these smart technologies, demonstrating that smart AM devices are a promising PSS family.

Egg allergy occurs frequently in childhood with a reported prevalence of 1.3–1.6%. Providing optimal care to egg-allergic patients requires knowledge of the most up-to-date developments in both diagnosis and management, as well as effective communication skills, which will engage the patient in the shared decision-making process. This review aims to provide up-to-date information on egg allergy and also serve as a concise guide on optimal patient diagnosis and management. The field of food allergy has seen multiple advances in recent years, including use of component resolved diagnostics, early egg introduction into the infant diet as a way of preventing egg allergy, baked egg introduction and oral immunotherapy as a form of active therapy. Faced with a variety of options and treatment paths, it is important to ensure that patients and families taking part in the decision-making process have fully understood the potential outcomes and trade-offs and can undertake a detailed discussion of all options that are available to them. Shared decision-making remains the cornerstone of optimal patient care.

WHAT IS KNOWN Estimates of the prevalence of diseases can be affected by non-biological factors such as change in diagnostic criteria and change in awareness of the diseases. The launch of a new drug is a potential contributory factor to the estimated prevalence of the target disease, but there appears to be no reports on this possible relationship. OBJECTIVE To investigate the relationship between the change in the number of patients diagnosed and factors such as the number of drugs indicated and the number of relevant scientific articles, focused on 45 intractable diseases in Japan. METHODS The number of patients with 1 of 45 intractable diseases from 2004 to 2013 was collected from publicly available data. The number of drugs indicated, the number of scientific articles, and diagnostic and certificate criteria for the disease were collected from publicly available sources from 2004 to 2013. Using these data, the correlation coefficient was calculated, and linear regression analyses were performed. RESULTS AND DISCUSSION The rate of increase in the number of drugs and the increase rate of the number of articles were found to be associated with an increase in the number of patients from 2004 to 2013. Linear regression analysis showed that the increase rate of the number of drugs available (2004-2008) was a statistically significant factor correlated with the rate of increase in the number of patients diagnosed in the following period (2009-2013). However, the increase rate of the number of patients (2004-2008) was not associated with the rate of increase in the number of drugs (2009-2013). One possible reason as to why the number of new drugs was correlated with the patient diagnosis numbers thereafter was proposed to be due to an increased awareness of diseases among physicians. WHAT IS NEW AND CONCLUSION This is the first study to investigate the relationship between the number of new drugs and the number of patients diagnosed based on data from multiple diseases. The investigation of data on 45 intractable diseases in Japan indicated that the increased availability of drugs was correlated with the increase in the number of patients in the future.

Over the past decades, the survival of breast cancer patients has significantly improved with advances in drug treatment and radiation therapy. Classical chemotherapy based primarily on anthracyclines, as well as targeted therapy and immunotherapy, have increased survival in breast cancer patients. However, both conventional chemotherapeutic agents and some new molecules can cause cardiovascular side effects that potentially worsen prognosis. The mechanisms of cardiovascular complications vary greatly depending on the drug type, and may manifest by temporary cardiomyocyte dysfunction or induce irreversible myocardial damage. Breast cancer patients are at high risk of cardiotoxicity because they receive combined cancer treatment and therefore require careful monitoring of heart function, especially with pre-existing cardiovascular diseases or multiple risk factors. Practically, it is possible due to close cooperation between cardiologists and oncologists, resulting in risk stratification of cardiovascular complications before antitumor treatment, individualized therapy, monitoring for early detection of complications, as well as timely use of cardioprotective therapy, both for prevention and treatment of complications that will improve survival and quality of life in breast cancer patients.

Nouf Al-Kahtani, Esra Al-Dhaif, Noor Alsaihtati, Khalid Farid, Suzan AlKhater Department of Health Information Management and Technology, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaCorrespondence: Nouf Al-KahtaniDepartment of Health Information Management and Technology, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaEmail nkalkahtani@iau.edu.saPurpose: The picture archiving and communication system (PACS) is one of the most important tools used in patient care in many hospitals worldwide. It allows clinicians to remotely communicate and consult with other clinicians on patient cases and view diagnostic images from different angles, thus facilitating patient diagnosis and treatment. Several studies have been conducted in Saudi Arabia to evaluate different aspects of PACS use; however, no comprehensive study has been conducted in its Eastern Province. This study aimed to investigate clinicians’ perceptions of the advantages and disadvantages of the use of PACS in Eastern Province hospitals in Saudi Arabia and identify the factors that affect their perceptions and its use. In addition, it aimed to gather recommendations of clinicians for improving the system and its implementation.Methods: A qualitative approach with grounded theory method was employed. A sample of 18 residents, radiologists, and consultants from three Eastern Province hospitals in Saudi Arabia participated in the study. Data were collected using semi-structured interviews over a period of 7 months.Results: The perceived advantages of PACS included providing quality images and the ability to manipulate their resolution, whereas the perceived barriers included low-speed internet connections and technical problems. Participants recommended providing clinicians remote access to the system and implementing a mobile PACS application. The theory that emerged from the analysis revealed that demographic, system-related, and hospital-related factors affected participants’ perspectives of PACS and its use.Conclusion: The results of this study and its theoretical model can help identify areas of improvement and inform policy and strategic planning for the effective implementation of PACS in patient care in Saudi Arabia.Keywords: radiology, healthcare technology, medical technology, patient care

Despite the overall success of using artificial intelligence (AI) to assist radiologists in performing computer-aided patient diagnosis, it remains challenging to build good models with small datasets at individual sites. Because many medical images do not come with proper labelling for training, this requires radiologists to perform strenuous labelling work and to prepare the dataset for training. Placing such demands on radiologists is unsustainable, given the ever-increasing number of medical images taken each year. We propose an alternative solution using a relatively new learning framework. This framework, called federated learning, allows individual sites to train a global model in a collaborative effort. Federated learning involves aggregating training results from multiple sites to create a global model without directly sharing datasets. This ensures that patient privacy is maintained across sites. Furthermore, the added supervision obtained from the results of partnering sites improves the global model’s overall detection abilities. This alleviates the issue of insufficient supervision when training AI models with small datasets. Lastly, we also address the major challenges of adopting federated learning.

Computed tomography perfusion (CTP) is crucial for acute ischemic stroke (AIS) patient diagnosis. To improve infarct prediction, enhanced image processing and automated parameter selection have been implemented in Vital Images’ new CTP+ software. We compared CTP+ with its previous version, commercially available software (RAPID and Sphere), and follow-up diffusion-weighted imaging (DWI). Data from 191 AIS patients between March 2019 and January 2020 was retrospectively collected and allocated into endovascular intervention ( n = 81) and conservative treatment ( n = 110) cohorts. Intervention patients were treated for large vessel occlusion, underwent mechanical thrombectomy, and achieved successful reperfusion of thrombolysis in cerebral infarction 2b/2c/3. Conservative treatment patients suffered large or small vessel occlusion and did not receive intravenous thrombolysis or mechanical thrombectomy. Infarct and penumbra were assessed using intervention and conservative treatment patients, respectively. Infarct and penumbra volumes were segmented from CTP+ and compared with 24-h DWI along with RAPID, Sphere, and Vitrea. Mean infarct differences (95% confidence intervals) and Spearman correlation coefficients (SCCs) between DWI and each CTP software product for intervention patients are: CTP+ = (5.8 ± 5.9 ml, 0.62), RAPID = (10.0 ± 5.2 ml, 0.73), Sphere = (3.0 ± 6.0 ml, 0.56), Vitrea = (7.2 ± 4.9 ml, 0.66). For conservative treatment patients, mean infarct differences and SCCs are: CTP+ = (–8.0 ± 5.4 ml, 0.64), RAPID = (–25.6 ± 11.5 ml, 0.60), Sphere = (–25.6 ± 8.0 ml, 0.66), Vitrea = (1.3 ± 4.0 ml, 0.72). CTP+ performed similarly to RAPID and Sphere in addition to its semi-automated predecessor, Vitrea, when assessing intervention patient infarct volumes. For conservative treatment patients, CTP+ outperformed RAPID and Sphere in assessing penumbra. Semi-automated Vitrea remains the most accurate in assessing penumbra, but CTP+ provides an improved workflow from its predecessor.

The Covid-19 virus appearing in Wuhan in December, 2019 and acting by binding to ACE-2 receptors in the respiratory system has caused many people to die by spreading to the whole world. The virus, which has been detected to spread faster than SARS-CoV and MERS-CoV viruses, spreads from person to person rapidly. People should not be together because it is transmitted from person to person through the respiratory tract. For this reason, it is recommended not to go to health institutions, provided that it is not urgent. However, many emergency patients go to dental clinics and hospitals for treatment. In this review; with possible contamination routes of Covid-19 such as airborne spread, contact spread and contaminated surface spread during patient diagnosis and treatment to prevent Covid-19 contamination, methods preventing spread such as hand hygiene, personal protective measures for dentists, intraoral rubber dam before dental procedures were indicated. It was also mentioned that the fast rotating tool with valves having negative pressure should be used and most importantly, the materials used in the dental clinic should be disposed in accordance with medical waste protocol. How to cite this article: Novel coronavirus disease 2019 (COVID-19) and challenges in dental practices. Int Dent Res 2020;10(3):100-15. https://doi.org/10.5577/intdentres.2020.vol10.n3.5 Linguistic Revision: The English in this manuscript has been checked by at least two professional editors, both native speakers of English.

Background & objectives: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) continues to be a devastating pandemic. This study was aimed at performance assessment of SARS-CoV-2 IgM and IgG ELISAs, and investigation of their utility for patient diagnosis and sero-epidemiologic investigations. Methods: Serum/plasma samples from COVID-19 patients or asymptomatic contacts (n=180) and healthy donors (n=90) were tested in parallel using two commercial IgM ELISAs (Erbalisa and Inbios), and four IgG ELISAs (Kavach, Euroimmun, Erbalisa and Inbios) along with an indigenous β-propiolactone inactivated virus-based ELISA (IRSHA-IgG-ELISA). Plaque reduction neutralization test (PRNT) was used as reference test. Results: Among 180 COVID-19 patients, 125 tested positive by PRNT. Inbios-IgM-ELISA showed sensitivity (Se)/specificity (Sp)/positive predictive value (PPV)/negative predictive value (NPV) of 93.6/97.8/98.4/94.4 per cent in relation to PRNT, and performed better than Erbalisa-IgM-ELISA (Se: 48%, Sp: 95.6%, PPV: 95.2%, NPV: 65.2%). During the first week of disease, only 47.4 per cent of the COVID-19 patients tested IgM positive by Inbios-IgM-ELISA, detection improving at two weeks and beyond (~86-100%). Among IgG tests, Inbios-IgG-ELISA ranked first in terms of sensitivity (83.2%), followed by IRSHA (64.8%), Euroimmun (64%), Erbalisa (57.6%) and Kavach (56%) tests. For all IgG tests, sensitivity improved during the third (73.9-95.7%) and fourth week (100%) of illness. The specificity (96.7-100%) and PPV (96.2-100%) of all IgG tests were high; NPV ranged between 71.9 and 87.1 per cent with Inbios-IgG-ELISA scoring highest. Interpretation & conclusions: Our results show that IgM detection by the current, most sensitive ELISAs cannot replace molecular diagnosis, but may aid as a supplement test. The available IgG tests are suitable for serosurveys for the assessment of previous virus exposure.

Human cancer is a multifaceted decease. Throughout the years several techniques and technologies have been proposed for the diagnosis and treatment of such deceases. However, the technological advances in the field of Information and Communication Technologies (ICT), Augmented Reality (AR), and Artificial Intelligence (AI), can facilitate during the diagnosis and prediction processes. This paper presents a conceptual framework for the visualization of Magnetic resonance imaging (MRI) scans, the data acquisition and analysis from patients based on AI algorithms and AR. Furthermore, the preliminary development of the framework is presented, and future developments are discussed based on the implications faced.

Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.

The micromechanical properties of soft tissue can be used as markers for the physiological state and function of the tissue. Deep understanding of the micromechanics of soft tissue, such as the oesophagus, is of great significance to the design of artificial oesophagi, endoscope materials and coatings for medical devices. Here, the micromechanical properties of oesophageal mucosa were studied under different loading rates, deflections, and dwell time by using atomic force microscopy. The micromechanical properties of soft tissue included elastic modulus, plasticity and adhesion force. Results showed that the micromechanical properties changed with increasing loading rate, deflection and dwell time. The micromechanical properties of oesophageal mucosa were related to time-dependent behaviours, such as contact stress, energy transformation, and strain gradient plasticity. Furthermore, the heterogeneity of oesophageal mucosa affected the micromechanical properties. The force mapping mode was a reliable and effective means to study the micromechanical properties of soft tissue. The results can provide a basis and technical support for the diagnosis of oesophageal diseases from a microscale as well as a material design perspective.

The lack of reliable diagnostic and/or prognostic biomarkers for multiple sclerosis (MS) is the major obstacle to timely and accurate patient diagnosis in MS patients. To identify new proteins associated with MS we performed a detailed proteomic analysis of cerebrospinal fluid (CSF) of patients newly diagnosed with relapsing-remitting MS (RRMS) and healthy controls. Reflecting significantly higher prevalence of MS in women we included only women patients and controls in the study. To eliminate a potential effect of therapy on the CSF composition, only the therapy-naïve patients were included. Pooled CSF samples were processed in a technical duplicate, and labeled with stable-isotope coded TMT tags. To maximize the proteome coverage, peptide fractionation using 2D-LC preceded mass analysis using Orbitrap Fusion Tribrid Mass Spectrometer. Differential concentration of selected identified proteins between patients and controls was verified using specific antibodies. Of the identified 900 CSF proteins, we found 69 proteins to be differentially abundant between patients and controls. In addition to several proteins identified as differentially abundant in MS patients previously, we observed several linked to MS for the first time, namely eosinophil-derived neurotoxin and Nogo receptor. Our data confirm differential abundance of several previously proposed protein markers, and provide indirect support for involvement of copper–iron disbalance in MS. Most importantly, we identified two new differentially abundant CSF proteins that seem to be directly connected with myelin loss and axonal damage via TLR2 signaling and Nogo-receptor pathway in women newly diagnosed with RRMS.

In this review, we present the current literature on the diagnosis and management of stress urinary incontinence induced by sphincter deficiency associated with neurogenic lower urinary tract dysfunction. Because sphincter deficiency stress urinary incontinence is not a common condition, there are no guidelines or large-scale clinical studies that focus on the diagnosis and management of this condition. Although there is consensus that urodynamic investigation is the only method to assess the function of the lower urinary tract, there is no established urodynamic criteria to diagnose neurogenic sphincter deficiency. As for the treatment of sphincter deficiency stress urinary incontinence in neurogenic patients, artificial urinary sphincter has a better success rate and a higher complication and reoperation rate than other methods including sling surgery and injection of bulking agents. Although there is a progress in the evaluation and management of neurogenic patients with sphincter deficiency stress urinary incontinence, a prospective multicenter clinical study on this condition should be highly recommended.

Blood transfusion is an important part of health care. A blood transfusion can be carried out after careful consideration of its risks and benefits. One of the important considerations for blood transfusion is adverse transfusion reaction. Several risk factors for a transfusion reaction are age, gender, repeated transfusion, disease diagnosis, type of blood component, and blood incompatibility. This research is a descriptive study performed at Blood Bank Dr. Wahidin Sudirohusodo Hospital, Makassar, from January to December 2017. The subjects were all patients reported to have an acute transfusion, totaling 104 subjects. The descriptive method was carried out by the calculation of frequency distribution. From January to December 2017, 104 patients with transfusion reactions were obtained, predominantly were male (53.8%), with the lowest age range of ≥ 60 years (27.9%), packed red cell as the most found blood components (82.4%), a history of previous transfusion reactions (72.1%), and a diagnosis of malignancy (53.9%). The history of repeated transfusions is the most common risk factor compared to age, gender, blood component, and patient diagnosis.

BACKGROUND Accurate quantitative data of the adult bony orbital volume and dimension are needed for treatment optimization. In the present study, the authors aim to evaluate adult orbital volume and corresponding linear dimensions according to age, volume, and individual symmetry. METHODS Seventy computerized tomography facial scans of adults were randomly chosen from an institutional database and 3-dimensionally reconstructed. Studies were excluded for orbital pathology or incomplete radiographic data. Anatomic landmarks were marked. Interval linear distances and orbital volumes were calculated. Data were analyzed using paired T-tests, independent T-tests, linear regression analysis, and 1-way analysis of variance. RESULTS A total of 140 orbits from 70 patients were analyzed (female = 35, male = 35), ranging from 20 to 88 years of age. Orbital volume was similar between an individual's left and right side; however, a difference was observed in vertical orbital height, orbital width, inferior orbital rim position, orbital roof length, orbital width, and medial orbital wall length (0.45, 0.64, 0.4, 0.77, 0.97, and 5.1 mm, respectively; P

When performing paediatric pancreatic cancer (PC) diagnosis, magnetic resonance imaging (MRI) will be interfered by many factors, resulting in poor diagnosis. In order to improve the diagnostic effect of MRI on PC, this study is based on machine learning and combines SVM (support vector machine) and MRI detection with the support of image processing technology and improves the diagnostic accuracy of MRI for PC by the computer-assisted method. At the same time, in the research, this study combines the characteristics of MRI detection to construct an MRI detection model based on SVM. In addition, this study obtains test samples through data collection, analyses the performance of the algorithm model through actual cases, and combines image processing to improve the detection effect. Studies have shown that the algorithm model proposed in this study can effectively improve the accuracy of MRI diagnosis of PC and provide a theoretical reference for subsequent related research.

Microfluidics is an upcoming field of science that is going to be used widely in many safety-critical applications including healthcare, medical research and defence. Hence, technologies for fault testing and fault diagnosis of these chips are of extreme importance. In this study, the authors propose a scalable pseudo-exhaustive testing and diagnosis methodology for flow-based microfluidic biochips. The proposed approach employs a divide-and-conquer based technique wherein, large architectures are split into smaller sub-architectures and each of these are tested and diagnosed independently.

Introduction: Antimicrobial agents are common employ in paediatric patients. Emergency department make good place for known prescribing pattern of antibiotics with frequent use for disease that interfere in treatment between private pharmacy & hospital. The irrational and overuse of antibiotic in last decades did not follow the international guidelines and infectious strategy which if not controlled or minimize will lead to higher rates of mortality in human societies. The aim was to find the magnitude of antibiotics prescribing in children internal emergency department at Basra hospital for Maternity and Children & showing relationship with misuse of it Methods: The study was performed on 560 paediatrics patients aged (1 month -13 years) that seen in the (Basra Hospital for Maternity and Children) emergency department during 4 months from December 2017 to March 2018. These cases were dividing according to containment antibiotic and several parameters such as patient diagnosis and number of antibiotics prescribed. Results: A total of 61% (n=342) patients were males. The average number of antibiotics per patient was 1.45 Prescription did not contain antibiotics 28.57% (n=160) Prescription contain antibiotics 71.42% (n =400) of all patient from total prescribed. Most cases diagnosis for prescribing antibiotics are (24%) Gastroenteritis (16.25%) bronchiolitis and (11.75%) pneumonia. Conclusion: The dispensing of antibiotics is not following constant or international guidelines which will cause problems like resistance and economic side also. Nearly almost the admitted patients received antibiotics regardless the culture results.

Introduction: In the last decade, multiparametric magnetic resonance imaging (MRI) has achieved tremendous advances in applications to amyotrophic lateral sclerosis (ALS) to increase the understanding of the associated pathophysiology. The aim of this review is to summarize recent progress in the development of MRI-based techniques aiming to support the clinical diagnosis in ALS.Areas covered: The review of structural and functional MRI applications to ALS and its variants (restricted phenotypes) is focused on the potential of MRI techniques which contribute to the diagnostic work-up of patients with the clinical presentation of a motor neuron disease. The potential of specific MRI methods for patient diagnosis and monitoring is discussed, and the future design of clinical MRI applications to ALS is conceptualized.Expert opinion: Current multiparametric MRI allows for the use as a clinical biological marker and a technical instrument in the clinical diagnosis of patients with ALS and also of patients with ALS variants. Composite neuroimaging indices of specific anatomical areas derived from different MRI techniques might guide in the diagnostic applications to ALS. Such a development of ALS-specific MRI-based composite scores with sufficient discriminative power versus ALS mimics at an individual level requires standardized advanced protocols and comprehensive analysis approaches.

In pathological diagnosis of breast cancer, there are problems such as shortage of pathologists, difficulties in sample labeling, and huge workload of manual diagnosis. Therefore, deep learning-based computer-assisted pathology analysis systems have been developed to diagnose breast cancer and have achieved impressive results. However, it is difficult to obtain a large number of training sets due to the scarcity of pathological images and the huge labeling costs. Therefore, the size of the training set should be planned before building the pathology computer-assisted breast cancer analysis system. Here, the authors present a study to determine the optimal size of the training data set needed to achieve high classification accuracy when developing a pathology computer-assisted breast cancer analysis system. The authors trained two kind of CNNs using six different sizes of training data set and then tested the resulting system with a total of 10,000 images. All images were acquired from the Camelyon17 challenge. Here, the authors propose a scheme for determining the size of the training set and the size of the model in developing the pathology computer-assisted breast cancer analysis systems, which can be easily applied to develop systems for other different pathological images.