An assessment of blood transfusion practice guidelines: What quality of indication is being employed to grow transfusion guideline endorsements?

Transfusion of blood components is widely utilized in the management of medical and surgical conditions. Though transfusion is a life-saving intervention, there has been debate about the standardization of blood transfusion practices. There has been a tremendous response in literature generated from multiple medical specialties regarding appropriate use of blood products to guide clinicians in their transfusion decisions. However, the consequence of numerous guidelines from multiple specialties results in varying recommendations for transfusion practices. This study was designed to compare and analyze current guidelines to determine if the recommendations generated to guide clinicians in transfusion decisions are truly supported by quality evidence. We performed a literature search on clinical transfusion practice guidelines from January 2005 to October 2015 with the following computer databases: PubMed/Medline, Cochrane Central, Scopus and the National Guideline Clearinghouse. Additional websites and publications, such as the Australian and New Zealand Society of Blood Transfusion, were also searched for guidelines missed from the computer database search. Key words that were used for the search include the combination of the following keywords: blood, blood component, blood product, transfusion, guidelines. The resulting eleven guidelines were analyzed for the following areas: characteristics and composition of the guideline working group panel, literature and evidence utilized for the systematic review, databases utilized to retrieve evidence and literature for the systematic review, methodologies employed by guideline committees to grade strength and quality of evidence and recommendations, quantity of recommendations suggested, and specific transfusion thresholds and/or clinical settings for transfusion of blood products. We developed a three-tiered classification system in order to compare the level of evidence and strength of recommendations generated by each guideline even with the utilization of seven difference grading systems. A total of 107 recommendations were generated about packed red blood cells, fresh frozen plasma, platelets, and cryoprecipitate transfusion. Of the 107 recommendations, 48 (48.86%) of the recommendations were specific to the use of packed red blood cells, 31 (28.97%) of the recommendations were specific to the use of fresh frozen plasma, 15 (12.02%) of the recommendations were specific for the use of platelets, and only 13 (12.15% recommendations were specific to the use of cryoprecipitate. Future research should thus be stimulated and directed at providing more abundant and high quality evidence regarding the use and safety of blood components in the perioperative setting

Positive Findings of Blood Cultures in Febrile Children Presenting with Thalassemia Major in a Tertiary Care Hospital

Objective: To assess status of bacterial pathogens in thalassemia major patients presenting with fever in a tertiary care hospital  Methods:  A retrospective descriptive study was conducted at –removed for blind review---from November, 2020 to October, 2021 in which 102 patients of thalassemia major who presented with fever and were suspicious of having septicemia were enrolled. Their demographic characteristics and pathological findings were noted in this study. Standardized approach was followed for blood withdrawal and sample collection required for performing blood cultures. The data was analyzed with SPSS version 21.  Results:  Females were in majority. Total 57 (56.0%) of patients had positive blood culture. The most frequent bacterial pathogen was Klebsiella pneumonia 13 (12.7%), followed by E-coli 11 (10.7%), staphylococcus aureus 7 (6.8%), pseudomonas 8 (7.8%) and streptococcus type pathogens 6 (5.8%).  Conclusion:  Positive culture findings were high in the thalassemia major patients who presented with fever. 

Antibiotic Resistance Pattern of Salmonella Species in Children in a Tertiary Care Hospital

Objective: To determine the recent antibiotic resistance patterns of Salmonella species in children in a tertiary care hospital. Materials and Methods: This descriptive cross-sectional study was conducted in the Department of Pediatrics of Akbar Niazi Teaching Hospital, Bhara Kahu, from 18th March 2020 to 31st January 2021. A total of one hundred and seventy-eight (n=178) patients of either gender having age between 1 month to 12 years who had signs and symptoms of enteric fever and positive blood culture report of Salmonella species were enrolled in this study through non-probability, consecutive sampling. The demographic characteristics of patients along with the antibiotic sensitivity pattern of the Salmonella species were recorded on the predesigned proforma and analyzed through SPSS version 25. Results: Out of the total of 178 patients, reports of blood cultures of 164(92.1%) patients showed Salmonella Typhi while those of 14(7.9%) patients showed Salmonella Paratyphi. Out of the total of 178 Salmonella cases, 11(6.2%) were extensively drug-resistant and 58(32.6%) were multidrug-resistant. All XDR cases were of Salmonella Typhi. Out of 14 Salmonella Paratyphi, 2(14%) were MDR but no XDR Salmonella Paratyphi was found.  Nalidixic acid had the highest resistance (96.4%) followed by sulphamethoxazole (65.5%). Azithromycin had the least resistance (0%) followed by imipenem (1.8%), piperacillin-tazobatam (1.9%), and meropenem (2%). Resistance of ceftriaxone was 20.8% and that of ciprofloxacin was 28.2%. Conclusion: No resistance of Salmonella species against azithromycin was found in our study. The presence of XDR typhoid fever cases and the rising resistance of Salmonella species to ceftriaxone and carbapenems are very alarming. To prevent this resistance, we should reserve carbapenems for complicated cases of resistant typhoid fever only. Minocycline and pipercillin-tazobactam are two other good cost-effective drugs in resistance to typhoid fever

TAQWA: Teaching Adolescents Quality Wadhu/Ablution contactlessly using deep learning

This research presents a unique and innovative approach to teaching young children the proper steps of ablution (wazoo/wudu) by utilizing a non-invasive sensing system integrated with deep learning algorithms. However, most existing ablution detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. We conducted experiments with a group of youngsters to evaluate the system’s effectiveness, demonstrating its potential in fostering a deeper appreciation and comprehension of religious practices among young learners. This innovative privacy-preserving ablution system employs state-of-the-art UWB-radar technology with advanced Deep Learning (DL) techniques to effectively address the challenges mentioned above. The core focus of this system is to categorize the four fundamental ablution steps: Wash Face 3x, Wash Hand 3x, Wash Head 1x, and Wash Feet 3x. By transforming the collected data into spectrograms and harnessing the sophisticated DL models VGG16 and VGG19, the proposed system accurately detects these ablution steps, achieving an impressive maximum accuracy of 97.92% across all categories with the utilization of VGG16

Contactless privacy-preserving head movement recognition using deep learning for driver fatigue detection

Head movement holds significant importance in con-veying body language, expressing specific gestures, and reflecting emotional and character aspects. The detection of head movement in smart or assistive driving applications can play an important role in preventing major accidents and potentially saving lives. Additionally, it aids in identifying driver fatigue, a significant contributor to deadly road accidents worldwide. However, most existing head movement detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. This novel privacy-preserving system utilizes UWB-radar technology and leverages Deep Learning (DL) techniques to address the mentioned issues. The system focuses on classifying the five most common head gestures: Head 45L (HL45), Head 45R (HR45), Head 90L (HL90), Head 90R (HR90), and Head Down (HD). By processing the recorded data as spectrograms and leveraging the advanced DL model VGG16, the proposed system accurately detects these head gestures, achieving a maximum classification accuracy of 84.00% across all classes. This study presents a proof of concept for an effective and privacy-conscious approach to head position classification.</p

Recognizing British sign language using deep learning: a contactless and privacy-preserving approach

Sign language is utilized by deaf-mute to communicate through hand movements, body postures, and facial emotions. The motions in sign language comprise a range of distinct hand and finger articulations that are occasionally synchronized with the head, face, and body. Automatic sign language recognition (SLR) is a highly challenging area and still remains in its infancy compared with speech recognition after almost three decades of research. Current wearable and vision-based systems for SLR are intrusive and suffer from the limitations of ambient lighting and privacy concerns. To the best of our knowledge, our work proposes the first contactless British sign language (BSL) recognition system using radar and deep learning (DL) algorithms. Our proposed system extracts the 2-D spatiotemporal features from the radar data and applies the state-of-the-art DL models to classify spatiotemporal features from BSL signs to different verbs and emotions, such as Help, Drink, Eat, Happy, Hate, and Sad. We collected and annotated a large-scale benchmark BSL dataset covering 15 different types of BSL signs. Our proposed system demonstrates highest classification performance with a multiclass accuracy of up to 90.07% at a distance of 141 cm from the subject using the VGGNet model

Balochi Speech Recognition using Android Based Smart Phone

The latest era of computers is called the Artificial Intelligence where multiple intelligent machines are working to ease the life of a common people. Interacting with machines via human language is one of the hot areas called Natural Language Processing (NLP). The various language speech recognition systems are already built and there is a need to build the speech recognition system for languages which are lacking in various computing resources. Balochi language is one of the Pakistani languages which lacks computing resources such as automatic speech recognition system. This paper presents the Balochi Speech Recognition system in which the Android mobile phone is controlled by speaking Balochi words. The Balochi speech recognition system calls or opens the various installed applications when the user speaks in Balochi language. For the sampling purpose a total of 230 subjects were selected to record the samples of 2300 words. These words have been recorded in various environments including silent and noisy environment. The android platform-based Balochi speech recognition system has been designed which takes input from Balochi speakers in Balochi language and performs some activities based on the proposed model. The Android application is designed to understand Balochi words. The system has been build using React Native technology. The Balochi speech recognition system has been tested for various Balochi words and produced an average accuracy of 89% and 81% for native and non-native speakers of Balochi language. The system is capable to be extended in various directions and to be applied in multiple area-based applications. Users who speak Balochi language will benefit from this Android application, which makes it easier to use smartphones using local language rather than having to speak English

Pushing the limits of remote RF sensing by reading lips under the face mask

The problem of Lip-reading has become an important research challenge in recent years. The goal is to recognise speech from lip movements. Most of the Lip-reading technologies developed so far are camera-based, which require video recording of the target. However, these technologies have well-known limitations of occlusion and ambient lighting with serious privacy concerns. Furthermore, vision-based technologies are not useful for multi-modal hearing aids in the coronavirus (COVID-19) environment, where face masks have become a norm. This paper aims to solve the fundamental limitations of camera-based systems by proposing a radio frequency (RF) based Lip-reading framework, having an ability to read lips under face masks. The framework employs Wi-Fi and radar technologies as enablers of RF sensing based Lip-reading. A dataset comprising of vowels A, E, I, O, U and empty (static/closed lips) is collected using both technologies, with a face mask. The collected data is used to train machine learning (ML) and deep learning (DL) models. A high classification accuracy of 95% is achieved on the Wi-Fi data utilising neural network (NN) models. Moreover, similar accuracy is achieved by VGG16 deep learning model on the collected radar-based dataset

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030