Birth weight estimation--a sonographic model for Pakistani population

OBJECTIVE: To develop a sonographic birth weight estimation model for Pakistani population and to validate the published models in the same population. METHODS: Data was collected for pregnant women who presented to Radiology Department of Aga Khan University Hospital Karachi from January 2007 to July 2008 and had undergone ultrasound estimation of foetal weight within 4 days prior to a term delivery (37-42 weeks gestation). The neonate\u27s actual birth weight was used to validate the published foetal weight estimation models and modified sonographic birth weight estimation model was derived for our population by using linear regression. RESULTS: Modified sonographic birth weight estimation model for our population was derived by using foetal parameters. No significant difference (p-value \u3e 0.05) of actual and predicted birth weight derived from Our regression model, Campbell and Woo models was noted, however least difference (p = 0.7) was identified between our predicted model (Mean difference 14 +/- 37.7 g). CONCLUSION: Our sonographic modified regression model of foetal weight estimation gave the least difference with actual neonatal birth weight and can be reliably used in our population. Hadlock1, Hadlock2 and Woo2 models are not appropriate in our setting or should be used carefully while predicting foetal weight in our population

The association between obesity, mortality and filling pressures in pulmonary hypertension patients; the “obesity paradox”

SummaryBackgroundThe term “obesity paradox”, refers to lower mortality rates in obese patients, and is evident in various chronic cardiovascular disorders. There is however, only scarce data regarding the clinical implication of obesity and pulmonary hypertension (PH). Therefore, in the current study, we evaluated the possible prognostic implications of obesity in PH patients.MethodsWe assessed 105 consecutive PH patients for clinical and hemodynamic parameters, focusing on the possible association between Body Mass Index (BMI) and mortality. Follow-up period was 19 ± 13 months.ResultsSixty-one patients (58%) had pre-capillary PH and 39 patients (37%) out-of-proportion post-capillary PH. During follow-up period, 30 patients (29%) died. Death was associated with reduced functional-class, inverse-relation with BMI, higher pulmonary artery and right atrial pressures, pulmonary vascular resistance and signs of right ventricular failure. In multivariate analysis, obesity (BMI ≥ 30 kg/m²), was the variable most significantly correlated with improved survival [H.R 0.2, 95% C.I 0.1–0.6; p = 0.004], even after adjustment for baseline characteristics. Obese and very-obese (BMI ≥ 35 kg/m²) patients had significantly less mortality rates during follow-up (12% and 8%, respectively) than non-obese patients (41%), p = 0.01. The tendency of survival benefit for the obese vs. non-obese patients was maintained both in the pre-capillary (10% vs. 46% mortality, p = 0.008) and disproportional post-capillary PH patients (11% vs. 40% mortality, p = 0.04).ConclusionsObesity was significantly associated with lower mortality in both pre-capillary and disproportional post-capillary PH patients. It seems that in PH, similarly to other chronic clinical cardiovascular disease states, there may be a protective effect of obesity, compatible with the “obesity paradox”

Unsupervised Landmark Discovery Using Consistency Guided Bottleneck

We study a challenging problem of unsupervised discovery of object landmarks. Many recent methods rely on bottlenecks to generate 2D Gaussian heatmaps however, these are limited in generating informed heatmaps while training, presumably due to the lack of effective structural cues. Also, it is assumed that all predicted landmarks are semantically relevant despite having no ground truth supervision. In the current work, we introduce a consistency-guided bottleneck in an image reconstruction-based pipeline that leverages landmark consistency, a measure of compatibility score with the pseudo-ground truth to generate adaptive heatmaps. We propose obtaining pseudo-supervision via forming landmark correspondence across images. The consistency then modulates the uncertainty of the discovered landmarks in the generation of adaptive heatmaps which rank consistent landmarks above their noisy counterparts, providing effective structural information for improved robustness. Evaluations on five diverse datasets including MAFL, AFLW, LS3D, Cats, and Shoes demonstrate excellent performance of the proposed approach compared to the existing state-of-the-art methods. Our code is publicly available at https://github.com/MamonaAwan/CGB_ULD.Comment: Accepted ORAL at BMVC 2023 ; Code: https://github.com/MamonaAwan/CGB_UL

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients

An overview of enhancing drought tolerance in cotton through manipulating stress resistance genes

Drought stress affects the normal growth of plant by influencing Physiological, morphological molecular and biochemical traits at cellular level. It is a polygenic trait, controlled by multiple genes, which makes its manipulation difficult by genetic engineering. It seems drought could be major threat in future to high yield of cotton in Pakistan as well around the globe because it is spontaneous and cannot be controlled with manuring and skilled agricultural practices. Gene manipulation could be a solution of this threat by producing transgenic cotton plants. As it is polygenic trait, so, understanding about cellular mechanism of drought tolerance is crucial to impart tolerance by controlling gene expression under stressed conditions. Universal Stress Proteins (USP) genes have already been identified in drought stressed leaves of Gossypium arboreum which make this variety of cotton a rich source of stress tolerance genes. USP genes could be manipulated for drought tolerant transgenic cotton with high yielding as well and it is most important family of proteins in this regard. This family encompasses a conserved group of proteins that has been reported in different organisms which are activating under various abiotic stress conditions. USP is also a regulatory protein; its activity can be increased by manipulating its interactions

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Elective cancer surgery in COVID-19-free surgical pathways during the SARS-CoV-2 pandemic: An international, multicenter, comparative cohort study

PURPOSE As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19–free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19–free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19–free surgical pathways. Patients who underwent surgery within COVID-19–free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19–free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score–matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19–free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION Within available resources, dedicated COVID-19–free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks