The oil tanker Tasman Spirit was grounded in the channel of the port of Karachi, Pakistan on 27, July 2003. The vessel was carrying a cargo of 67,535 tones of Iranian Light crude oil for delivery to the Pakistan Refinery Limited in Karachi when the grounding occurred. Significant quantities of oil were spilled when the Tasman Spirit broke up during the evening of August 13, 2003. By 18 August approximately 27,000 tones of cargo had been lost. The coastal environment in which the Tasman Spirit oil spill (TSOS) occurred is a rich and diverse tropical marine/estuarine ecosystem. It includes extensive mangrove forests, habitat for sea turtles, dolphins, porpoises, and beaked whales, and several species of lizards and sea snakes. The initial findings revealed that the initial impacted area covered about 1600 square kilometer and a coast line of 7.5 kilometer. Pakistan does not have the expertise to deal with oil spill disaster of this magnitude. The rapid assessment report was prepared with the assistance of United Nations Development Programme, United Nations Environment Programme and local experts. The report emphasized the need of carrying out a Natural Resource Damage Assessment (NRDA). This paper highlights important findings of the NRDA study describing the methodologies adapted for the systematic assessment of the extent and severity of the environmental damage and ecological injury resulting from the Tasman Spirit Oil Spill.
Objective: Congenital heart defects (CHD) are still missed despite nearly universal prenatal ultrasound screening programs, which may result in severe morbidity or even death. Deep machine learning (DL) can automate image recognition from ultrasound. The aim of this study was to apply a previously developed DL model trained on images from a tertiary center, to fetal ultrasound images obtained during the second-trimester standard anomaly scan in a low-risk population. Methods: All pregnancies with isolated severe CHD in the Northwestern region of the Netherlands between 2015 and 2016 with available stored images were evaluated, as well as a sample of normal fetuses' examinations from the same region. We compared initial clinical diagnostic accuracy (made in real time), model accuracy, and performance of blinded human experts with access only to the stored images (like the model). We analyzed performance by study characteristics such as duration, quality (independently scored by study investigators), number of stored images, and availability of screening views. Results: A total of 42 normal fetuses and 66 cases of isolated CHD at birth were analyzed. Of the abnormal cases, 31 were missed and 35 were detected at the time of the clinical anatomy scan (sensitivity 53 percent). Model sensitivity and specificity was 91 and 93 percent, respectively. Blinded human experts (n=3) achieved sensitivity and specificity of 55+/-10 percent (range 47-67 percent) and 71+/-13 percent (range 57-83 percent), respectively. There was a statistically significant difference in model correctness by expert-grader quality score (p=0.04). Abnormal cases included 19 lesions the model had not encountered in its training; the model's performance (15/19 correct) was not statistically significantly different on previously encountered vs. never before seen lesions (p=0.07). Conclusions: A previously trained DL algorithm out-performed human experts in detecting CHD in a cohort in which over 50 percent of CHD cases were initially missed clinically. Notably, the DL algorithm performed well on community-acquired images in a low-risk population, including lesions it had not been previously exposed to. Furthermore, when both the model and blinded human experts had access to stored images alone, the model outperformed expert humans. Together, these findings support the proposition that use of DL models can improve prenatal detection of CHD.
Purpose: To estimate the prevalence of computer vision syndrome and evaluate the effects of interventions applied to computer users in a tertiary care hospital. Materials and Methods: In our study, 102 eyes of 51 people (non-medicos) with desk job using computers/mobile were taken as the study group. The Schirmer test, tear breakup time (TBUT) and ocular surface disease index (OSDI) were evaluated. Accordingly, they were given treatment and followed up. Results: In our study, we included 51 subjects who were a regular user of mobile and computers. Mean screen time was 6.08 ± 1.5 hours. Before treatment, the mean Schirmer’s, TBUT and OSDI test were 8.85 ± 1.2 mm (range 5.5–11.5 mm), 7.64 ± 2.4 seconds (range 4.0–12.5 seconds) and 30.47 ± 13.1 (range 10.40–62.50), respectively. The prevalence of dry eye was 58%, according to OSDI severity grading. After treatment, the Schirmer1, TBUT and OSDI tests showed improvement and the results were highly significant (p < 0.001). Conclusions: It is important to optimize the exposure time and improve awareness among users. Its high time now, every institution should come up with a few guidelines in concern with the high screen time of the desk job worker.
Introduction: To study the incidence, visual outcomes, andanterior segment anatomical outcomes of intraoperativecomplications of cataract surgery.Materials and Methods: This is a prospective observationalhospital-based study conducted in the department ofOphthalmology SRMS IMS, Bareilly, Uttar Pradesh. Inthis study, 70 patients were studied for 1.5 yrs from 1st ofNovember 2019 to 13th of April 2021. All these patients withintraoperative complications were evaluated preoperatively andpost-operatively for vision and anterior segment anatomicaloutcomes.Results: In this study, the commonest intraoperativecomplication was Injury to Iris/ Iridodialysis (52.9%). Basedon pre-operative vision, the patients had 5/60 and 6/36 visionwith 30.0%, each followed by 6/24 (14.3%) and 3/60 (12.9%).Corneal hazy/edema was reduced to 10.0% at one-monthfollow-up from 52.9% on post-operative day one. Also, DMfolds were reduced to 11.4% from 75.7% at day one.Conclusions: In this study, visual outcome of the cataractsurgery was much better, and there is a significant progressionin the visual acuity. Routine monitoring of the visual outcomeof the cataract surgery at each hospital would go in long-wayto enhance both the quantity and quality of the surgery andthus decrease the substantial amount of burden of blindnesson our country.
To study the ophthalmic co-morbidities and post- COVID ophthalmic complications in mild to moderate COVID positive patients. This was a questionnaire based prospective longitudinal study conducted between August 2020 and December 2020. In the first phase, an ophthalmologist in personal protective equipment (PPE) physically visited the patients and a pre-designed structured questionnaire regarding any ophthalmic complaints was filled and scoring was done later. In the second phase, the patients were telephonically interviewed after 3-6 months of their discharge from the hospital, regarding the development of ocular symptoms for which they needed to consult an ophthalmologist and the treatment taken was noted. 9% of the total 77 patients included in the study reported severe symptoms (scores between 17-24/24). On comparing the mean questionnaire scores (out of 24) it was seen that more severe ophthalmic complaints were seen in patients aged >= 50 years than <50 years (11.35 vs 5.75, p<0.05), moderate category than mild category patients (11.70 vs 3.63, p<0.05), patients with systemic co-morbidities than those who had none (11.48 vs 4.04, p<0.05) and in patients who later needed to consult an ophthalmologist due to development of one or more complications than those who did not.(13.27 vs 6.63, p<0.05).Post- COVID complications were seen in 27 patients (35%). They included progression of pre-existing ocular disease like cataract, glaucoma, diabetic and hypertensive retinopathy, and new diagnosis of diabetic and hypertensive retinopathy and HCQ- related maculopathy. Ocular co-morbidities should be looked for in every COVID patient. Those at higher risk of developing complications, should undergo a detailed ophthalmic examination after they are discharged from the isolation wards. Hospitals need to work on capacity building and/or look for alternatives, like telemedicine, to ensure timely eye care to all patients.
While prenatal congenital heart disease (CHD) screening has improved, accuracy remains as low as 30 percent. Standard fetal biometrics—cardiac axis (CA), cardiothoracic ratio (CTR), RV fractional area change (FAC), LV FAC, RA:LA area ratio, RV:LV area ratio—are available from screening imaging and can each aid in CHD screening, but can be cumbersome to measure. Combinations of biometrics may offer further utility but are challenging to integrate at the point of care. We tested whether using these biometrics in combination has utility in CHD screening (normal vs. abnormal). Further, we tested whether automatically predicted biometrics could function similarly to manually-labeled biometrics for this purpose. We included 105 fetal echocardiograms (20 normal, 85 abnormal across 12 different CHD lesions). We manually calculated the six biometrics above, performed dimensionality reduction using principal component analysis, and then clustered the resulting data by K-means. A previously developed deep learning model (Arnaout et al Nature 2021) was also used to automatically predict biometrics for normal, tetralogy of Fallot, and hypoplastic left heart syndrome hearts and plotted on the above cluster map. Optimal number of clusters was four, with RV:LV ratio and CTR as the most important features distinguishing clusters. Cluster 1 was predominantly normal hearts with cluster 2-4 largely abnormal hearts (Figure 1). The sensitivity and specificity for predicting abnormal hearts (e.g. CHD) was 86% and 75%, respectively. Model-predicted biometrics landed in the same clusters as the manually labeled lesions (Figure 1). To our knowledge, this is the first use of clustering to provide visualization of multiple fetal cardiac biometrics at once and reveal diagnostic utility. Once tested in screening ultrasounds on a larger scale, clustering of automated biometrics may be clinically useful at the screening point of care to augment scalable population-based screening.
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