An oral carbon monoxide-releasing molecule protects against acute hyperhemolysis in sickle cell disease.

ABSTRACT: Acute hyperhemolysis is a severe life-threatening complication in patients with sickle cell disease (SCD) that may occur during delayed hemolytic transfusion reaction (DHTR), or vaso-occlusive crises associated with multiorgan failure. Here, we developed in vitro and in vivo animal models to mimic endothelial damage during the early phase of hyperhemolysis in SCD. We then used the carbon monoxide (CO)-releasing molecule CORM-401 and examined its effects against endothelial activation, damage, and inflammation inflicted by hemolysates containing red blood cell membrane-derived particles. The in vitro results revealed that CORM-401: (1) prevented the upregulation of relevant proinflammatory and proadhesion markers controlled by the NF-κB enhancer of activated B cells, and (2) abolished the expression of the nuclear factor erythroid-2-related factor 2 (Nrf2) that regulates the inducible antioxidant cell machinery. We also show in SCD mice that CORM-401 protects against hemolysate-induced acute damage of target organs such as the lung, liver, and kidney through modulation of NF-κB proinflammatory and Nrf2 antioxidant pathways. Our data demonstrate the efficacy of CORM-401 as a novel therapeutic agent to counteract hemolysate-induced organ damage during hyperhemolysis in SCD. This approach might be considered as possible preventive treatment in high-risk situations such as patients with SCD with history of DHTR.

Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial.

OBJECTIVES: Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent directly to front-line providers would reduce escalations. DESIGN: Single-center prospective pragmatic nonrandomized clustered clinical trial. SETTING: Academic tertiary care medical center. PATIENTS: Adult patients admitted to four medical-surgical units. Assignment to intervention or control arms was determined by initial unit admission. INTERVENTIONS: Real-time alerts stratified according to predicted likelihood of deterioration sent either to the primary team or directly to the rapid response team (RRT). Clinical care and interventions were at the providers' discretion. For the control units, alerts were generated but not sent, and standard RRT activation criteria were used. MEASUREMENTS AND MAIN RESULTS: The primary outcome was the rate of escalation per 1000 patient bed days. Secondary outcomes included the frequency of orders for fluids, medications, and diagnostic tests, and combined in-hospital and 30-day mortality. Propensity score modeling with stabilized inverse probability of treatment weight (IPTW) was used to account for differences between groups. Data from 2740 patients enrolled between July 2019 and March 2020 were analyzed (1488 intervention, 1252 control). Average age was 66.3 years and 1428 participants (52%) were female. The rate of escalation was 12.3 vs. 11.3 per 1000 patient bed days (difference, 1.0; 95% CI, -2.8 to 4.7) and IPTW adjusted incidence rate ratio 1.43 (95% CI, 1.16-1.78; p < 0.001). Patients in the intervention group were more likely to receive cardiovascular medication orders (16.1% vs. 11.3%; 4.7%; 95% CI, 2.1-7.4%) and IPTW adjusted relative risk (RR) (1.74; 95% CI, 1.39-2.18; p < 0.001). Combined in-hospital and 30-day-mortality was lower in the intervention group (7% vs. 9.3%; -2.4%; 95% CI, -4.5% to -0.2%) and IPTW adjusted RR (0.76; 95% CI, 0.58-0.99; p = 0.045). CONCLUSIONS: Real-time machine learning alerts do not reduce the rate of escalation but may reduce mortality.

Association between Early Basal Ganglia and Thalami Perfusion Assessed by Color Doppler Ultrasonography and Brain Injury in Infants with Hypoxic-Ischemic Encephalopathy: A Prospective Cohort Study.

OBJECTIVE: To evaluate associations between neurologic outcomes and early measurements of basal ganglia (BG) and thalamic (Th) perfusion using color Doppler ultrasonography (CDUS) in infants with hypoxic-ischemic encephalopathy (HIE). STUDY DESIGN: Prospective study of infants with mild (n = 18), moderate (n = 17), and severe HIE (n = 14) and controls (n = 17). Infants with moderate-severe HIE received therapeutic hypothermia (TH). CDUS was performed at 24-36 hours and brain magnetic resonance imaging (MRI) at a median of 10 days. Development was followed through 2.5-5 years. The primary outcome was the association between BG and Th perfusion and brain MRI injury. Secondary analyses focused on associations between perfusion measurements and admission neurologic examinations, MRI scores in infants treated with TH, and motor and sensory disability, or death. An exploratory analysis assessed the accuracy of BG and Th perfusion to predict brain MRI injury in infants treated with TH. RESULTS: Increased BG and Th perfusion on CDUS was observed in infants with severe MRI scores and those with significant motor and neurosensory disability or death through 2.5-5 years (P < .05). Infants with severe HIE showed increased BG and Th perfusion (P < .005) compared with infants with moderate HIE. No differences were identified between the between the control and mild HIE groups. Th perfusion ≥0.237 cm/second (Area under the curve of 0.824) correctly classified 80% of infants with severe MRI scores. CONCLUSIONS: Early dynamic CDUS of the BG and Th is a potential biomarker of severe brain injury in infants with HIE and may be a useful adjunct to currently used assessments.

PKR Mediates the Mitochondrial Unfolded Protein Response through Double-Stranded RNA Accumulation under Mitochondrial Stress.

Mitochondrial stress, resulting from dysfunction and proteostasis disturbances, triggers the mitochondrial unfolded protein response (UPRMT), which activates gene encoding chaperones and proteases to restore mitochondrial function. Although ATFS-1 mediates mitochondrial stress UPRMT induction in C. elegans, the mechanisms relaying mitochondrial stress signals to the nucleus in mammals remain poorly defined. Here, we explored the role of protein kinase R (PKR), an eIF2α kinase activated by double-stranded RNAs (dsRNAs), in mitochondrial stress signaling. We found that UPRMT does not occur in cells lacking PKR, indicating its crucial role in this process. Mechanistically, we observed that dsRNAs accumulate within mitochondria under stress conditions, along with unprocessed mitochondrial transcripts. Furthermore, we demonstrated that accumulated mitochondrial dsRNAs in mouse embryonic fibroblasts (MEFs) deficient in the Bax/Bak channels are not released into the cytosol and do not induce the UPRMT upon mitochondrial stress, suggesting a potential role of the Bax/Bak channels in mediating the mitochondrial stress response. These discoveries enhance our understanding of how cells maintain mitochondrial integrity, respond to mitochondrial dysfunction, and communicate stress signals to the nucleus through retrograde signaling. This knowledge provides valuable insights into prospective therapeutic targets for diseases associated with mitochondrial stress.

The Use of a Production Function to Evaluate Caribbean Ocean Health Conflicts and Economic Development: Policy Implications.

Central American and Caribbean (CAC) countries enjoy diverse marine environments. The oceans that enclose these coastlines contribute significantly to their economic growth. Industrial expansion and tourism place pressure on the marine ecosystems causing a decline in ocean health. To evaluate the cause of ocean health changes we investigated the factors influencing CAC ocean health using a health production function. Using Rank-Based Regression and the set of extractive, cultural and human well-being and services goals measuring Ocean Health Index (OHI), data from the World Bank, and the Human Development Index we developed a production function for CAC countries ocean health. Results show that all regional OHI scores, but Tourism and Recreation, the main income earning industry for most of the CAC countries, are less than the global score with four of the goals less than 40. The production function shows that all the goals, but the biodiversity sub-goal species, positively influencing OHI. Two climatic related variables, Nitrous Oxide and Carbon Dioxide negatively influenced OHI. The results are important to policy makers as they decide on the need to make greater effort towards improving sustainable contribution of CAC ocean resources to the blue economy.

Monitoring drought pattern for pre- and post-monsoon seasons in a semi-arid region of western part of India.

Drought has become a regular phenomenon in the western semi-arid regions of India, where severe drought occurs once in 8-9 years. Therefore, two drought indices, namely temperature condition index (TCI) and vegetation condition index (VCI), were prepared from using Landsat datasets to appraise and monitor of drought pattern for the pre- and post-monsoon seasons between 1996 and 2016 in the Latur district, the north-western part of India. Additionally, the average frequency layers (AFL) of all drought and land use indices were prepared to analyse the correlation between them. The results show a substantial increase in the area under high, very high and severe drought classes both pre- and post-monsoon seasons during the study period. The highest increase was noticed from the high drought class from 2532.45 to 4792.49 sq. km and 1559.84 to 3342.32 sq. km for pre- and post-monsoon season, respectively, based on the TCI and 1269.81 to 1787.77 sq. km in very high drought class for the post-monsoon season using the VCI. The correlation analysis showed that there exists a significant relationship between the land use indices and drought indices. However, the spatial pattern of correlation was heterogeneous for both pre- and post-monsoon seasons. The results of this research can help in the drought management and mitigation planning in the study area. In addition, a similar approach may be applied to analyse drought patterns in other places with similar geographic characteristics as both VCI and TCI are cost-effective and less time-consuming methods and produce reliable outcomes.

Estimated US Infection- and Vaccine-Induced SARS-CoV-2 Seroprevalence Based on Blood Donations, July 2020-May 2021.

Importance: People who have been infected with or vaccinated against SARS-CoV-2 have reduced risk of subsequent infection, but the proportion of people in the US with SARS-CoV-2 antibodies from infection or vaccination is uncertain. Objective: To estimate trends in SARS-CoV-2 seroprevalence related to infection and vaccination in the US population. Design, Setting, and Participants: In a repeated cross-sectional study conducted each month during July 2020 through May 2021, 17 blood collection organizations with blood donations from all 50 US states; Washington, DC; and Puerto Rico were organized into 66 study-specific regions, representing a catchment of 74% of the US population. For each study region, specimens from a median of approximately 2000 blood donors were selected and tested each month; a total of 1 594 363 specimens were initially selected and tested. The final date of blood donation collection was May 31, 2021. Exposure: Calendar time. Main Outcomes and Measures: Proportion of persons with detectable SARS-CoV-2 spike and nucleocapsid antibodies. Seroprevalence was weighted for demographic differences between the blood donor sample and general population. Infection-induced seroprevalence was defined as the prevalence of the population with both spike and nucleocapsid antibodies. Combined infection- and vaccination-induced seroprevalence was defined as the prevalence of the population with spike antibodies. The seroprevalence estimates were compared with cumulative COVID-19 case report incidence rates. Results: Among 1 443 519 specimens included, 733 052 (50.8%) were from women, 174 842 (12.1%) were from persons aged 16 to 29 years, 292 258 (20.2%) were from persons aged 65 years and older, 36 654 (2.5%) were from non-Hispanic Black persons, and 88 773 (6.1%) were from Hispanic persons. The overall infection-induced SARS-CoV-2 seroprevalence estimate increased from 3.5% (95% CI, 3.2%-3.8%) in July 2020 to 20.2% (95% CI, 19.9%-20.6%) in May 2021; the combined infection- and vaccination-induced seroprevalence estimate in May 2021 was 83.3% (95% CI, 82.9%-83.7%). By May 2021, 2.1 SARS-CoV-2 infections (95% CI, 2.0-2.1) per reported COVID-19 case were estimated to have occurred. Conclusions and Relevance: Based on a sample of blood donations in the US from July 2020 through May 2021, vaccine- and infection-induced SARS-CoV-2 seroprevalence increased over time and varied by age, race and ethnicity, and geographic region. Despite weighting to adjust for demographic differences, these findings from a national sample of blood donors may not be representative of the entire US population.

Effects of lockdown due to COVID-19 outbreak on air quality and anthropogenic heat in an industrial belt of India.

Highly urbanized and industrialized Asansol Durgapur industrial belt of Eastern India is characterized by severe heat island effect and high pollution level leading to human discomfort and even health problems. However, COVID-19 persuaded lockdown emergency in India led to shut-down of the industries, traffic system, and day-to-day normal work and expectedly caused changes in air quality and weather. The present work intended to examine the impact of lockdown on air quality, land surface temperature (LST), and anthropogenic heat flux (AHF) of Asansol Durgapur industrial belt. Satellite images and daily data of the Central Pollution Control Board (CPCB) were used for analyzing the spatial scale and numerical change of air quality from pre to amid lockdown conditions in the study region. Results exhibited that, in consequence of lockdown, LST reduced by 4.02 °C, PM10 level decreased from 102 to 18 µg/m3 and AHF declined from 116 to 40W/m2 during lockdown period. Qualitative upgradation of air quality index (AQI) from poor to very poor state to moderate to satisfactory state was observed during lockdown period. To regulate air quality and climate change, many steps were taken at global and regional scales, but no fruitful outcome was received yet. Such lockdown (temporarily) is against economic growth, but it showed some healing effect of air quality standard.

Prospective research in infants with mild encephalopathy identified in the first six hours of life: neurodevelopmental outcomes at 18-22 months.

BACKGROUND: Studies of early childhood outcomes of mild hypoxic-ischemic encephalopathy (HIE) identified in the first 6 h of life are lacking. OBJECTIVE: To evaluate neurodevelopmental outcomes at 18-22 months of PRIME study. STUDY DESIGN: Multicenter, prospective study of mild HIE defined as ≥1 abnormality using the modified Sarnat within 6 h of birth and not meeting cooling criteria. Primary outcome was disability with mild: Bayley III cognitive 70-84 or ≥85 and either Gross Motor Function Classification System (GMFCS) 1 or 2, seizures, or hearing deficit; moderate: cognitive 70-84 and either GMFCS 2, seizures, or hearing deficit; severe: cognitive <70, GMFCS 3-5. RESULTS: Of the 63 infants enrolled, 51 (81%) were evaluated at 19 ± 2 months and 43 (68%) completed Bayley III. Of the 43 infants, 7 (16%) were diagnosed with disability, including 1 cerebral palsy and 2 autism. Bayley scores < 85 in either cognition, motor, or language were detected in 17 (40%): 14 (32%) language, 7 (16%) cognitive, and 6 (14%) motor domain. Infants with disability had more abnormalities on discharge examination and brain MRI, with longer hospital stay (p < 0.001). CONCLUSIONS: In this contemporary untreated cohort of mild HIE, disability occurred in 16% of infants at 18-22 months.

Glycosyl-Substituted Dicarboxylates as Detergents for the Extraction, Overstabilization, and Crystallization of Membrane Proteins.

To tackle the problems associated with membrane protein (MP) instability in detergent solutions, we designed a series of glycosyl-substituted dicarboxylate detergents (DCODs) in which we optimized the polar head to clamp the membrane domain by including, on one side, two carboxyl groups that form salt bridges with basic residues abundant at the membrane-cytoplasm interface of MPs and, on the other side, a sugar to form hydrogen bonds. Upon extraction, the DCODs 8 b, 8 c, and 9 b preserved the ATPase function of BmrA, an ATP-binding cassette pump, much more efficiently than reference or recently designed detergents. The DCODs 8 a, 8 b, 8 f, 9 a, and 9 b induced thermal shifts of 20 to 29 °C for BmrA and of 13 to 21 °C for the native version of the G-protein-coupled adenosine receptor A2A R. Compounds 8 f and 8 g improved the diffraction resolution of BmrA crystals from 6 to 4 Å. DCODs are therefore considered to be promising and powerful tools for the structural biology of MPs.

Contamination levels and potential sources of organic pollution in an Asian river.

The Houjing River has long been an environmental victim of economic development. Industries that have settled along the bank of this river may have largely contributed to severe organic wastes pollution. This study collected water and sediment samples at various points along the river and measured concentrations of 61 volatile organic compounds (VOCs) and 128 semi-volatile organic compounds (SVOCs) for a period of 16 months (Feb 2014-June 2015). Our analyses show that elevated levels of VOCs were observed near two industrial areas, Dashe and Renwu industrial parks. High SVOC concentrations were found in the vicinities of the Nanzih Export Processing Zone (NEPZ) and CingPu station, possibly due to considerable effluent discharges of adjacent industrial and residential areas. Comparing this study's findings with the standard values of different governmental agencies and studies similar to this one, the ecosystem of the Houjing River was seriously contaminated. This study could be used by the government as a basis for future and urgent pollution prevention actions aimed at protecting this ecosystem and reducing the negative impacts of these contaminants on the health and well-being of the local residents and the environment.

Specific activation, signalling and secretion profiles of human platelets following PAR-1 and PAR-4 stimulation.

Blood platelets play a central haemostatic function; however, they also play a role in inflammation and are capable of secreting various cytokines, chemokines and related products. The purpose of this study was to identify subtle variations in platelet physiology using proteomics. We compared the levels of membrane proteins (n = 3), α and δ granule proteins (n = 18), and signalling proteins (n = 30) from unstimulated platelets with those of protease-activated receptor (PAR)-1- and PAR-4-stimulated platelets (n = 10). The vast majority of these proteins responded similarly to PAR-1 or PAR-4 engagement. However, differences were observed within membrane CD40L expressed, and α granule GRO-α and MDC secreted proteins.

Immune-reactive soluble OX40 ligand, soluble CD40 ligand, and interleukin-27 are simultaneously oversecreted in platelet components associated with acute transfusion reactions.

BACKGROUND: Leukoreduction of labile blood components dramatically decreases the frequency of minor, intermediate, and severe adverse events (AEs), referred to as acute transfusion reactions (ATRs), especially after transfusion of platelet components (PCs). The pathophysiology of AEs may result from accumulation of soluble, secreted, platelet (PLT) factors with proinflammatory functions stored in PCs. Thus, several cosynergizing factors associated with PLT accumulation in PCs may contribute to clinically reported ATRs with inflammatory symptoms. STUDY DESIGN AND METHODS: We screened for 65 PLT-associated secretory products in PCs that caused ATRs and identified PLT molecules associated with ATRs and inflammation. A functional in vitro study using PC supernatants assayed on reporting immune cells was performed to indicate relevance. RESULTS: Among 10,600 apheresis PCs, 30 caused inflammatory ATRs and contained significantly elevated levels of soluble CD40 ligand (sCD40L), interleukin (IL)-27, and soluble OX40 ligand (sOX40L). Normal PLTs secreted IL-27 and sOX40L at bioactive concentrations upon thrombin stimulation and were up regulated in association with ATRs, similar to sCD40L. Other secreted products were identified but not investigated further as their positivity was not consistent. CONCLUSIONS: This study demonstrates the putative participation of PLT-derived sOX40L, IL-27, and sCD40L, which accumulate in PC supernatants, with inflammatory-type ATRs. Further studies are required to determine the clinical significance of these findings to forecast preventive measures whenever possible.

Sustainable urea treatment by environmental-friendly and highly hydrophilic vesicle-like iron phosphate-based carbon.

Despite the versatile potential applications of urea, its unfavorable characteristics for conventional treatment methods hinder its utilization. Therefore, this study developed vesicle-like iron phosphate-based carbon (IP@C400) as a breakthrough urea removal and recovery material for a wide range of urea-containing sources. IP@C400 rapidly exhibited an exceptional capacity (2242 mg/g in 1 h) across a wide range of pH, even in synthetic hemodialysis wastewater with high urea concentrations and diverse co-existing components, compared with the 60 prominent adsorbents. The adsorption process followed dual Pseudo-kinetic, Langmuir-isotherm models with the involvement of primary robust physical (i.e., H-bonding and electrostatic interaction) and chemical mechanisms (i.e., hydrolysis). Remarkably, IP@C400 can maintain high urea removal (90 %) or recovery efficiency (95 %) even after 10 cycles with minimal leakages of Fe and P (far below WHO and EUWFD standards)-a significant improvement over disposable options. IP@C400 could also perform efficiently on batch and a new approach integrating with a naturally accessible material based on the fixed-bed column using low-range urea realistic samples, achieving 65.2 L water over 10 cycles with undetected urea, neutral pH, and well-aligned water safety standards with a minimal adsorbent dose (0.1 g.L-1) and economical cost ($0.05 L-1). Lastly, its environmentally friendly nature, which contains essential nutrients for plant growth, further enhances its recyclability after release. Thus, IP@C400 offers a solution to environmental sustainability and the urgent ultrapure water issue that industries are facing.

Z-number based fuzzy MCDM models for analyzing non-traditional security threats to finance supply chains: A case study from Vietnam.

Non-traditional security (NTS) threats have a vast and profound impact on many aspects of economic, political, social, and many other areas, especially supply chain finance (SCF), particularly in countries like Vietnam, which potentially affects the economic efficiency of businesses' supply chain financial, thereby affecting the general economy of the country and the world. In order to prevent and minimize the negative impacts caused by NTS threats to SCF, this study was conducted to identify NTS threats affecting SCF in Vietnam, at the same time calculate the weight of the impact level and find out the cause and effect relationship between them. Solution strategies are also proposed and ranked, thereby serving as a reference basis for relevant parties to choose appropriate response solutions. Due to the multi-criteria nature of NTS threats, the multiple criteria decision-making (MCDM) method is used in combination with the Z-number concept and Fuzzy set theory to approach the problem of certainty and increase the accuracy of study. The NTS threats are first identified through a literature review and then validated for suitability using the DELPHI technique (DELPHI). Suitable threats will be determined by relationship, weighted by Decision Making Trial And Evaluation Laboratory (DEMATEL) method. Proposed strategies are ranked using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The results indicate that there are 19 NTS factors affecting SCF in Vietnam, and the global economic downturn, pandemic and health crisis, financial crisis and cybersecurity risk are the four root cause factors with the most decisive influence. Businesses and concerns need to prioritize addressing these four threats because they not only have a strong impact but also entail many other threats. The two strategies considered to be the most effective are a sustainable practice and a risk-hedging strategy. Businesses, governments, and stakeholders also should pay attention to the macroeconomic environment, technology, and environment and build sustainable businesses, regularly monitoring economic fluctuations and creating plans to prevent risks.

Exploring the associations of tobacco smoking and serum cotinine levels with selected inflammatory markers in adults with HIV in South Africa.

This study examined the associations between tobacco smoking and serum cotinine levels, an objective biochemical measure of tobacco smoke exposure, with markers of inflammation, i.e., interferon-gamma (IFN-γ), interleukin 10 (IL-10), interleukin 2 (IL-2) and tumour necrosis factor-alpha (TNF-α) in people living with HIV (PLWH).These specific markers were selected because of their hypothesised associations with smoking, PLWH and their outcomes. In a random sample of ≥ 18-year-old PLWH receiving care at 17 public healthcare facilities across the Western Cape Province in South Africa, data collection included self-reported smoking history, and serum levels of cotinine and selected inflammatory markers. The inflammatory marker data were log transformed because of the skewedness of their distribution. Linear regression models (1) adjusted for age and gender, and (2) fully adjusted for age, gender, current alcohol use, body mass index and CD4 counts were used to examine the associations between smoking tobacco or serum cotinine and inflammatory markers. Level of significance was p < 0.05. Among 749 PLWH who were mainly women (79%), the mean age was 38.5 (8.9) years and similar when stratified by smoking status. Serum cotinine levels exhibited a striking discrepancy, with a median of 154 ng/mL among current smokers, in stark contrast to the consistent median values of 10 ng/mL observed among past and never smokers. In regression models adjusted for age and gender, current smoking and frequent smoking were associated with lower IL-2 but higher TNF-α. Log-cotinine exhibited associations with IFN-γ, IL-10, and TNF-α, while cotinine levels ≥ 10 ng/mL compared to < 10 ng/mL were associated with higher IFN-γ and TNF-α. In fully adjusted models, log-cotinine and cotinine levels ≥ 10 ng/mL displayed significant associations with higher IFN-γ and lower IL-2. This study underscores the importance of investigating the interplay between smoking tobacco or serum cotinine levels with pro-inflammatory cytokines in PLWH. It signals the need for comprehensive research to unravel the potential synergistic impacts of smoking tobacco and HIV infection on chronic inflammation and immune dysregulation, shedding light on critical avenues for intervention and management strategies.

Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in Patients Undergoing Invasive Mechanical Ventilation.

The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have been developed; however, these fail to capture the wide spectrum of data available. Here, we develop and validate a deep learning-based model using routinely collected chest X-rays to predict the outcome of attempted extubation. We included 2288 serial patients admitted to the Medical ICU at an urban academic medical center, who underwent invasive mechanical ventilation, with at least one intubated CXR, and a documented extubation attempt. The last CXR before extubation for each patient was taken and split 79/21 for training/testing sets, then transfer learning with k-fold cross-validation was used on a pre-trained ResNet50 deep learning architecture. The top three models were ensembled to form a final classifier. The Grad-CAM technique was used to visualize image regions driving predictions. The model achieved an AUC of 0.66, AUPRC of 0.94, sensitivity of 0.62, and specificity of 0.60. The model performance was improved compared to the Rapid Shallow Breathing Index (AUC 0.61) and the only identified previous study in this domain (AUC 0.55), but significant room for improvement and experimentation remains.

Prevalence and influences of diabetes and prediabetes among adults living with HIV in Africa: a systematic review and meta-analysis.

INTRODUCTION: In people living with human immunodeficiency virus (PLHIV), traditional cardiovascular risk factors, exposure to HIV per se and antiretroviral therapy (ART) are assumed to contribute to cardiometabolic diseases. Nevertheless, controversy exists on the relationship of HIV and ART with diabetes. To clarify the relationship between HIV and type 2 diabetes, this review determined, in PLHIV in Africa, diabetes and prediabetes prevalence, and the extent to which their relationship was modified by socio-demographic characteristics, body mass index (BMI), diagnostic definitions used for diabetes and prediabetes, and HIV-related characteristics, including CD4 count, and use and duration of ART. METHODS: For this systematic review and meta-analysis (PROSPERO registration CRD42021231547), a comprehensive search of major databases (PubMed-MEDLINE, Scopus, Web of Science, Google Scholar and WHO Global Health Library) was conducted. Original research articles published between 2000 and 2021 in English and French were included, irrespective of study design, data collection techniques and diagnostic definitions used. Observational studies comprising at least 30 PLHIV and reporting on diabetes and/or prediabetes prevalence in Africa were included. Study-specific estimates were pooled using random effects models to generate the overall prevalence for each diagnostic definition. Data analyses used R statistical software and "meta" package. RESULTS: Of the 2614 records initially screened, 366 full-text articles were assessed for eligibility and 61 were selected. In the systematic review, all studies were cross-sectional by design and clinic-based, except for five population-based studies. Across studies included in the meta-analysis, the proportion of men was 16-84%. Mean/median age was 30-62 years. Among 86,412 and 7976 participants, diabetes and prediabetes prevalence rates were 5.1% (95% CI: 4.3-5.9) and 15.1% (9.7-21.5). Self-reported diabetes (3.5%) was lower than when combined with biochemical assessments (6.2%; 7.2%). DISCUSSION: While not statistically significant, diabetes and prediabetes were higher with greater BMI, in older participants, urban residents and more recent publications. Diabetes and prediabetes were not significantly different by HIV-related factors, including CD4 count and ART. CONCLUSIONS: Although HIV-related factors did not modify prevalence, the diabetes burden in African PLHIV was considerable with suboptimal detection, and likely influenced by traditional risk factors. Furthermore, high prediabetes prevalence foreshadows substantial increases in future diabetes in African PLHIV.

A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation.

BACKGROUND: Early prediction of the need for invasive mechanical ventilation (IMV) in patients hospitalized with COVID-19 symptoms can help in the allocation of resources appropriately and improve patient outcomes by appropriately monitoring and treating patients at the greatest risk of respiratory failure. To help with the complexity of deciding whether a patient needs IMV, machine learning algorithms may help bring more prognostic value in a timely and systematic manner. Chest radiographs (CXRs) and electronic medical records (EMRs), typically obtained early in patients admitted with COVID-19, are the keys to deciding whether they need IMV. OBJECTIVE: We aimed to evaluate the use of a machine learning model to predict the need for intubation within 24 hours by using a combination of CXR and EMR data in an end-to-end automated pipeline. We included historical data from 2481 hospitalizations at The Mount Sinai Hospital in New York City. METHODS: CXRs were first resized, rescaled, and normalized. Then lungs were segmented from the CXRs by using a U-Net algorithm. After splitting them into a training and a test set, the training set images were augmented. The augmented images were used to train an image classifier to predict the probability of intubation with a prediction window of 24 hours by retraining a pretrained DenseNet model by using transfer learning, 10-fold cross-validation, and grid search. Then, in the final fusion model, we trained a random forest algorithm via 10-fold cross-validation by combining the probability score from the image classifier with 41 longitudinal variables in the EMR. Variables in the EMR included clinical and laboratory data routinely collected in the inpatient setting. The final fusion model gave a prediction likelihood for the need of intubation within 24 hours as well. RESULTS: At a prediction probability threshold of 0.5, the fusion model provided 78.9% (95% CI 59%-96%) sensitivity, 83% (95% CI 76%-89%) specificity, 0.509 (95% CI 0.34-0.67) F1-score, 0.874 (95% CI 0.80-0.94) area under the receiver operating characteristic curve (AUROC), and 0.497 (95% CI 0.32-0.65) area under the precision recall curve (AUPRC) on the holdout set. Compared to the image classifier alone, which had an AUROC of 0.577 (95% CI 0.44-0.73) and an AUPRC of 0.206 (95% CI 0.08-0.38), the fusion model showed significant improvement (P<.001). The most important predictor variables were respiratory rate, C-reactive protein, oxygen saturation, and lactate dehydrogenase. The imaging probability score ranked 15th in overall feature importance. CONCLUSIONS: We show that, when linked with EMR data, an automated deep learning image classifier improved performance in identifying hospitalized patients with severe COVID-19 at risk for intubation. With additional prospective and external validation, such a model may assist risk assessment and optimize clinical decision-making in choosing the best care plan during the critical stages of COVID-19.