Bacterial and fungal co-infections among ICU COVID-19 hospitalized patients in a Palestinian hospital: a retrospective cross-sectional study

Background: Diagnosis of co-infections with multiple pathogens among hospitalized coronavirus disease 2019 (COVID-19) patients can be jointly challenging and essential for appropriate treatment, shortening hospital stays and preventing antimicrobial resistance. This study proposes to investigate the burden of bacterial and fungal co-infections outcomes on COVID-19 patients. It is a single center cross-sectional study of hospitalized COVID-19 patients at Beit-Jala hospital in Palestine. Methods: The study included 321 hospitalized patients admitted to the ICU between June 2020 and March 2021 aged ≥20 years, with a confirmed diagnosis of COVID-19 via reverse transcriptase-polymerase chain reaction assay conducted on a nasopharyngeal swab. The patient's information was gathered using graded data forms from electronic medical reports. Results: The diagnosis of bacterial and fungal infection was proved through the patient’s clinical presentation and positive blood or sputum culture results. All cases had received empirical antimicrobial therapy before the intensive care unit (ICU) admission, and different regimens during the ICU stay. The rate of bacterial co-infection was 51.1%, mainly from gram-negative isolates (Enterobacter species and K.pneumoniae). The rate of fungal co-infection caused by A.fumigatus was 48.9%, and the mortality rate was 8.1%. However, it is unclear if it had been attributed to SARS-CoV-2 or coincidental. Conclusions: Bacterial and fungal co-infection is common among COVID-19 patients at the ICU in Palestine, but it is not obvious if these cases are attributed to SARS-CoV-2 or coincidental, because little data is available to compare it with the rates of secondary infection in local ICU departments before the pandemic. Comprehensively, those conclusions present data supporting a conservative antibiotic administration for severely unwell COVID-19 infected patients. Our examination regarding the impacts of employing antifungals to manage COVID-19 patients can work as a successful reference for future COVID-19 therapy.

Evaluation of two AI software for the diagnosis of TB infection : A study protocol (Preprint)

BACKGROUND Tuberculosis (TB) profile in Malaysia showed an average annual growth rate of 2.23%, with an estimated 92 cases per 100,000 people reported in 2018. CXR remains the best conventional method for the early detection of pulmonary TB infection. The intervention of AI in TB diagnosis could efficiently aid human interpreters and reduce health professionals' work burden. To date, no evaluation of AI studies has been carried out in Malaysia. OBJECTIVE This study aims to determine the diagnostic accuracy and evaluate the performance of Qure.ai and Putra Analytica AI software. METHODS We will conduct a retrospective case-control study in Respiratory Medicine Institute (IPR), Kuala Lumpur Health Clinic and Bandar Botanik Klang Health Clinic. Patients' medical reports on TB investigation will be retrieved by accessing electronic and hardcopy medical records and collecting demographic data. Prior to conducting the study, patients' PTB status will be obtained by identifying MTB culture (reference standard) results in order to create a case and a control group. A total of 2000 CXR images will be retrieved, of which 1000 images will be the case (abnormality). Normal and abnormal CXR will be categorized into film and digital CXR, which will be screened onto the said AI software (index tests). RESULTS Results obtained from the AI software will be compared with the reference standard, and significant statistical analysis will be computed CONCLUSIONS We hope that the findings of this evaluation study will provide sufficient information for stakeholders and to implement AI technology in the medical imaging field for better management of TB in hospital and clinic settings.

Estimating the Mode of Delivery through Cause Analysis A Systematic Literature Review on the context of Reducing Cesarean

In developing countries, many health workers force pregnant women in private hospitals having baby through cesarean delivery, even though most pregnant mothers are skeptical about the risk of going under such operations. Doctors often encourage pregnant mothers to have a cesarean on the basis of their physical condition and medical reports. Emergency cesarean patient cannot be prepared in advance but on real time, If a system can be developed that can forecast whether a woman need natural or cesarean delivery, then rate of the risk of the pregnant women get reduce. To design such system, we need to know what factors influences the doctors to choose cesarean over normal delivery. We have conducted a systematic review in well-known databases to understand the various factors of pregnant mothers. A total of 19 studies were selected among 2512 studies based on the relevancy to the research objective. The objective of this study is to predict the mode of delivery based on 10 specific parameters identified separately from 180 parameters present in various test reports.

Article 7: The Right to a Name, Nationality, and to Know and Be Cared for by Parents

Abstract‘Government should provide birth certificates and medical reports to let the children know where they came from.’ (Asia-Pacific)

Modeling Digital Healthcare Services Using NLP and IoT in Smart Cities

Smart cities shall have computerized health services to help the treatment of health issues based on a centralized framework. These computerized healthcare services would involve the patient, medical support staff, and doctors. They would have voice recognition clinical staff management, smart health monitoring devices, smart medical reports, and smart medical treatment based on the internet of things (IoT) and natural language processing (NLP). This chapter shall describe the digital healthcare services in the context of NLP and IoT that would help in the improvement of quality of life for people living in smart cities. The authors shall discuss a possible case study on NLP and IoT in digital healthcare in the smart cities.

Malaria Parasite Detection in Thick Blood Smears using Deep Learning

Malaria parasitized detection is very important to detect as there are so many deaths due to false detection of malaria in medical reports. So analysis has gained a lot of attention in recent years. Detection of malaria is important as fast as possible because detecting malaria is difficult in blood smears. Our idea is to build a transfer learning model and detect the thick blood smears whether the presence of malaria parasites in a drop of blood. The data consists of 5000 each infected and uninfected data obtained from the NIH website. In this paper, I propose to use three different types of neural networks for the performance evaluation of the malaria data by transfer learning using CNN, VGG19, and fine-tuned VGG19. Transfer learning model performed well among various other models by achieving a precision of 98 percent and an f-1 score of 96 percent.

Injuries in Medium to Long-Distance Triathlon: A Retrospective Analysis of Medical Conditions Treated in Three Editions of the Ironman Competition

Triathlon’s popularity is rapidly increasing, and epidemiological data relating to its related medical conditions is crucial to the development of proper medical plans and safety guidelines for it. This study examined the data from the medical reports collected during three consecutive editions of Ironman Italy, from 2017 to 2019. Out of 10,653 race-starters, 3.3% required medical attention sustaining 472 medical conditions. A significantly higher injury risk was found for females versus males (χ2 = 9.78, p = 0.02) and in long-distance (IR: 4.09/1,000hours) rather than in Olympic/middle distance races (IR: 1.75/1,000hours). Most (68.4%) conditions (including muscular exhaustion, hypothermia, and dehydration) were systemic, whilst only 10.2% were acute traumatic injuries. Of a total of 357 triathletes requiring medical assistance, 8.1% were a candidate for hospitalisation. The equipment and personnel that are required for the medical assistance in future triathlon events were estimated based on Maurer’s algorithm, and ten practical recommendations for triathlon medical support were formulated.

Beware, Madness!

Рецензируемая книга Александра Михайловича Терехина «Сумасшествия в музыкальном театре: опера, балет», изданная в 2020 году, уникальна во многих отношениях. Во-первых, интерес вызывает личность самого автора. Профессиональная и творческая деятельность Терехина связана с медициной и музыкой. Врач-психиатр с сорокалетним стажем, Терехин более четверти века работал в Мариинском театре в качестве артиста миманса. Во-вторых, тема сумасшествия (безумия) еще не становилась самостоятельным предметом изучения в российских музыковедческих исследованиях. В-третьих, оригинальность книги составляют медицинские заключения. В центре внимания врача — тексты оперных и балетных либретто, на основе которых автор раскрывает причины помешательства и ставит различные диагнозы персонажам музыкального театра. Рассматривая конкретные клинические случаи, Терехин прибегает к профессиональным медицинским терминам. Среди диагнозов отметим такие, как реактивный параноид у Лючии («Лючия ди Ламмермур» Г. Доницетти), шизофрения у Мельника («Русалка» А. С. Даргомыжского, интоксикационный (гашишный) психоз у Солора («Баядерка» Л. Минкуса). Книга А. М. Терехина, не претендуя на всеохватность освещения темы сумасшествия (безумия), открывает музыковедам новые грани междисциплинарного подхода в изучении оперного и балетного репертуара. Alexander Mikhailovich Terekhin’s peer-reviewed book Madness in Musical Theater: Opera, Ballet, published in 2020, is unique in many ways. Firstly, the author’s personality is of interest. Terekhin’s professional and creative activity is connected with medicine and music. A psychiatrist with forty years of experience, Terekhin worked for more than a quarter of a century at the Mariinsky Theater as a mimance artist. Secondly, the theme of madness (insanity) has not yet become an independent subject of study in Russian musicological studies. Thirdly, the originality of the book is based on medical reports. The doctor focuses on the texts of opera and ballet librettos, on the basis of which the author reveals the causes of insanity and makes various diagnoses to the characters of the musical theater. Considering a specific clinical case, Terekhin resorts to professional medical terms. Among some diagnoses we can mention: reactive paranoid in Lucia (Lucia di Lammermoor by Donizetti), schizophrenia in Melnik (Rusalka by Dargomyzhsky, intoxicational (hashish) psychosis in Solor (La Bayadere by Minkus). Without claiming to cover the topic of insanity (insanity) comprehensively the book by Terekhin, opens up new facets of the interdisciplinary approach in studying opera and ballet repertoire to musicologists.

Comparative Analysis of Vertical Transmission of SARS-CoV-2 Antibodies in Vaccinated and Non-Vaccinated Pregnant Women

Pregnancy is a risk factor for developing a severe, complicated form of COVID-19. Medical reports have revealed that pregnancy increases three times the risk of ICU admission and 1.7 times the risk of death in patients with COVID-19. The crossing of the placenta by the antibodies generated through vaccination offer a level of protection that should not be ignored. We aimed to comparatively analyze the levels of SARS-CoV-2 IgG and IgM antibodies in pregnant women who have had this infection during pregnancy or have undergone a complete vaccination cycle during pregnancy, as well as antibody levels in newborns. The inclusion criterion was history of SARS-CoV-2 infection during pregnancy or COVID-19 complete vaccination. For each case the peri-partum values of IgG and IgM SARSCoV- 2 antibodies were analyzed in the same laboratory along with those of their newborns. The vaccination rate in our study group was about 6%. All cases had a significant value of protective IgG SARS-CoV-2 antibodies and the level of protective antibodies of the newborns closely followed maternal values. From the cases with SARS-CoV-2 infection during pregnancy, only 16.6% had a protective level of antibodies and 75% of the newborns from these cases had protective levels of IgG SARS-CoV-2 antibodies. Our results clearly plead in favor of vaccination in pregnancy which provides significant benefits for both mothers and infants.