A mathematical model to serve as a clinical tool for assessing obstructive sleep apnea severity

Obstructive sleep apnea (OSA) is a sleep disorder caused by periodic airway obstructions and has been associated with numerous health consequences, which are thought to result from tissue hypoxia. However, challenges in the direct measurement of tissue-level oxygenation make it difficult to analyze the hypoxia exposure pattern in patients. Furthermore, current clinical practice relies on the apnea-hypopnea index (AHI) and pulse oximetry to assess OSA severity, both of which have limitations. To overcome this, we developed a clinically deployable mathematical model, which outputs tissue-level oxygenation. The model incorporates spatial pulmonary oxygen uptake, considers dissolved oxygen, and can use time-dependent patient inputs. It was applied to explore a series of breathing patterns that are clinically differentiated. Supporting previous studies, the result of this analysis indicated that the AHI is an unreliable indicator of hypoxia burden. As a proof of principle, polysomnography data from two patients was analyzed with this model. The model showed greater sensitivity to breathing in comparison with pulse oximetry and provided systemic venous oxygenation, which is absent from clinical measurements. In addition, the dissolved oxygen output was used to calculate hypoxia burden scores for each patient and compared to the clinical assessment, highlighting the importance of event length and cumulative impact of obstructions. Furthermore, an intra-patient statistical analysis was used to underscore the significance of closely occurring obstructive events and to highlight the utility of the model for quantitative data processing. Looking ahead, our model can be used with polysomnography data to predict hypoxic burden on the tissues and help guide patient treatment decisions

Evaluating Unenhanced Multidetector Computed Tomography of Kidneys, Ureters and Bladder (CT KUB) as the Initial Imaging Service in Suspected Acute Renal Colic Patients

Objective: To assess the role of unenhanced multidetector computed tomography (CT) of kidneys, ureters and bladder (KUB) in the initial imaging of suspected acute renal colic. Study Design: Retrospective longitudinal study. Place and Duration of Study: Combined Military Hospital, Kharian Pakistan, from Jan 2020 to Jan 2021. Methodology: One hundred and thirty-eight cases of suspected acute renal colic underwent CT-KUB. The demographic,radiological, clinical, and follow-up data were recorded for each patient. Results: There were 88(51.8%) males and 82(48.2%) females in the present study, with a mean age of 50.86±18.57 years. Out of 170 patients, only 138(81.17%) were indicated with acute findings, whereas 32(18.82%) individuals showed no acute findings.The mean stone size was found to be 4.77±0.98mm. Most of the stones had a location near the pelvic brim (n=47; 34.15%). Conclusion: The use of CT KUB should be encouraged for the evaluation of renal colic. Keywords: Acute renal colic, Computed tomography (CT), Computed tomography of Kidneys, ureter and bladder (CT-KUB)

Global Retinoblastoma Presentation and Analysis by National Income Level.

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs

The global retinoblastoma outcome study : a prospective, cluster-based analysis of 4064 patients from 149 countries

DATA SHARING : The study data will become available online once all analyses are complete.BACKGROUND : Retinoblastoma is the most common intraocular cancer worldwide. There is some evidence to suggest that major differences exist in treatment outcomes for children with retinoblastoma from different regions, but these differences have not been assessed on a global scale. We aimed to report 3-year outcomes for children with retinoblastoma globally and to investigate factors associated with survival. METHODS : We did a prospective cluster-based analysis of treatment-naive patients with retinoblastoma who were diagnosed between Jan 1, 2017, and Dec 31, 2017, then treated and followed up for 3 years. Patients were recruited from 260 specialised treatment centres worldwide. Data were obtained from participating centres on primary and additional treatments, duration of follow-up, metastasis, eye globe salvage, and survival outcome. We analysed time to death and time to enucleation with Cox regression models. FINDINGS : The cohort included 4064 children from 149 countries. The median age at diagnosis was 23·2 months (IQR 11·0–36·5). Extraocular tumour spread (cT4 of the cTNMH classification) at diagnosis was reported in five (0·8%) of 636 children from high-income countries, 55 (5·4%) of 1027 children from upper-middle-income countries, 342 (19·7%) of 1738 children from lower-middle-income countries, and 196 (42·9%) of 457 children from low-income countries. Enucleation surgery was available for all children and intravenous chemotherapy was available for 4014 (98·8%) of 4064 children. The 3-year survival rate was 99·5% (95% CI 98·8–100·0) for children from high-income countries, 91·2% (89·5–93·0) for children from upper-middle-income countries, 80·3% (78·3–82·3) for children from lower-middle-income countries, and 57·3% (52·1-63·0) for children from low-income countries. On analysis, independent factors for worse survival were residence in low-income countries compared to high-income countries (hazard ratio 16·67; 95% CI 4·76–50·00), cT4 advanced tumour compared to cT1 (8·98; 4·44–18·18), and older age at diagnosis in children up to 3 years (1·38 per year; 1·23–1·56). For children aged 3–7 years, the mortality risk decreased slightly (p=0·0104 for the change in slope). INTERPRETATION : This study, estimated to include approximately half of all new retinoblastoma cases worldwide in 2017, shows profound inequity in survival of children depending on the national income level of their country of residence. In high-income countries, death from retinoblastoma is rare, whereas in low-income countries estimated 3-year survival is just over 50%. Although essential treatments are available in nearly all countries, early diagnosis and treatment in low-income countries are key to improving survival outcomes.The Queen Elizabeth Diamond Jubilee Trust and the Wellcome Trust.https://www.thelancet.com/journals/langlo/homeam2023Paediatrics and Child Healt

Glacial lakes mapping using multi satellite PlanetScope imagery and deep learning

Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3-4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. 71km2 in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring.International Foundation for Science ; COMSTECH gran

A mathematical model to serve as a clinical tool for assessing obstructive sleep apnea severity.

Obstructive sleep apnea (OSA) is a sleep disorder caused by periodic airway obstructions and has been associated with numerous health consequences, which are thought to result from tissue hypoxia. However, challenges in the direct measurement of tissue-level oxygenation make it difficult to analyze the hypoxia exposure pattern in patients. Furthermore, current clinical practice relies on the apnea-hypopnea index (AHI) and pulse oximetry to assess OSA severity, both of which have limitations. To overcome this, we developed a clinically deployable mathematical model, which outputs tissue-level oxygenation. The model incorporates spatial pulmonary oxygen uptake, considers dissolved oxygen, and can use time-dependent patient inputs. It was applied to explore a series of breathing patterns that are clinically differentiated. Supporting previous studies, the result of this analysis indicated that the AHI is an unreliable indicator of hypoxia burden. As a proof of principle, polysomnography data from two patients was analyzed with this model. The model showed greater sensitivity to breathing in comparison with pulse oximetry and provided systemic venous oxygenation, which is absent from clinical measurements. In addition, the dissolved oxygen output was used to calculate hypoxia burden scores for each patient and compared to the clinical assessment, highlighting the importance of event length and cumulative impact of obstructions. Furthermore, an intra-patient statistical analysis was used to underscore the significance of closely occurring obstructive events and to highlight the utility of the model for quantitative data processing. Looking ahead, our model can be used with polysomnography data to predict hypoxic burden on the tissues and help guide patient treatment decisions

Do Natural Disasters Cause Economic Growth? An ARDL Bound Testing Approach

This article strives to work out the causal relationship between natural disasters and economic growth in Pakistan. The study empirically tests the linkage using econometric techniques autoregressive distributed lag bound model by Pesaran (2001) and Granger causality test. We develop a proxy for the loss of natural disasters by a similar method as Noy (2009) and Bergholt et.al, (2012) did. The results of ARDL bounds testing approach evidence a negative long run relationship between the proxies of natural disasters and economic growth. The results of Granger Causality depict the uni-directional causality from natural disasters to economic growth both in short-run and long-run. Overall, the study determines that natural disasters deteriorate economic growth in Pakistan. This is the first study in Pakistan to assess the causal relationship among natural disasters and economic growth. So, further empirical evidence may link natural disasters to microeconomics and financial indicators. In future, researchers might control the impact of foreign development aid, remittances, political stability and country’s corruption rating. Natural disasters are an alarming issue and, addressing the questions related to their impacts on welfare of human being and economic growth of the countries contain significant importance in order to attract the attention of global development agencies and policymakers. As per INFORM (2015) risk index, Pakistan has the highest vulnerability towards natural disasters after Afghanistan. So, the study contains more significant value in context of Pakistan

Identification and application of biocontrol agents against Cotton leaf curl virus disease in Gossypium hirsutum under greenhouse conditions

Biological control is a novel approach in crop protection. Bacteria, such as Bacillus spp. and Pseudomonas spp., are reported for this purpose and some of their products are already commercially available. In this study, the rhizosphere and phyllosphere of healthy cotton plants were used as a source of bacterial isolates with properties of potential biocontrol agents. The isolates were screened for phosphate solubilization activity, indole acetic acid (IAA) production and antifungal activity. Two isolates, S1HL3 and S1HL4, showed phosphate solubilization and IAA production simultaneously, while another two, JS2HR4 and JS3HR2, demonstrated potential to inhibit fungal pathogens. These bacteria were identified as Pseudomonas aeruginosa (S1HL3), Burkholderia sp. (S1HL4) and Bacillus sp. (JS2HR4 and JS3HR2) based on biochemical and molecular characteristics. The isolates were tested against Cotton leaf curl virus (CLCuV) in greenhouse conditions, both as individual bacterial isolates and consortia. Treated plants were healthy as compared to control plants, where up to 74% of the plants were symptomatic for CLCuV infection. Maximum inhibition of CLCuV was observed in the plants treated with a mixture of bacterial isolates: the viral load in the treated plants was only 0.4% vs. up to 74% in controls. This treatment consortium included P. aeruginosa S1HL3, Burkholderia sp. S1HL4 and Bacillus spp. isolates, JS2HR4 and JS3HR2. The principal-component biplot showed a highly significant correlation between the viral load percentage and the disease incidence

Green synthesized silver nanoparticles using carrot extract exhibited strong antibacterial activity against multidrug resistant bacteria

Antimicrobial resistance is a worldwide problem. Pathogenic microorganisms develop antibiotic resistance as a result of exposure to inappropriate quantity of antibiotics and accumulation of mutations. Bio-friendly nanomaterial scan be developed as antimicrobial agents. Present study aimed to investigate the activity of biosynthesized silver nanoparticles (AgNPs) obtained from carrot extract of three different regions namely Gilgit, Haripur and Sheikhupura from Pakistan, having significant difference in climate and altitude. UV–visible spectroscopy, Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis were carried out for the morphological characterization of synthesized nanoparticles. Analysis of XRD revealed crystalline nature of AgNPs and average size calculated was about 22, 17 and 10 nm from Gilgit, Haripur and Sheikhupura samples respectively. The antibacterial efficacy of AgNPs was carried out against 7 American type culture collection strains of pathogenic bacteria and three macrolide resistant clinical isolates with promising results. Moreover, antioxidant activity indicated maximum DPPH radical scavenging effects as 47 % using a concentration of 250 μg/mL. Hemolysis assays indicated biocompatibility of AgNPs at lower concentration like 7 μg/mL and 15 μg/mL. Comparative analysis of bioactivity from different sites of sampling had indicated minute differences which may be due to the change in altitude, soil texture and other local environmental changes