BACKGROUND Roux-en-Y gastric bypass(RYGB)is a widely recognized bariatric procedure that is particularly beneficial for patients with class III obesity.It aids in significant weight loss and improves obesity-related m...BACKGROUND Roux-en-Y gastric bypass(RYGB)is a widely recognized bariatric procedure that is particularly beneficial for patients with class III obesity.It aids in significant weight loss and improves obesity-related medical conditions.Despite its effectiveness,postoperative care still has challenges.Clinical evidence shows that venous thromboembolism(VTE)is a leading cause of 30-d morbidity and mortality after RYGB.Therefore,a clear unmet need exists for a tailored risk assessment tool for VTE in RYGB candidates.AIM To develop and internally validate a scoring system determining the individualized risk of 30-d VTE in patients undergoing RYGB.METHODS Using the 2016–2021 Metabolic and Bariatric Surgery Accreditation Quality Improvement Program,data from 6526 patients(body mass index≥40 kg/m^(2))who underwent RYGB were analyzed.A backward elimination multivariate analysis identified predictors of VTE characterized by pulmonary embolism and/or deep venous thrombosis within 30 d of RYGB.The resultant risk scores were derived from the coefficients of statistically significant variables.The performance of the model was evaluated using receiver operating curves through 5-fold cross-validation.RESULTS Of the 26 initial variables,six predictors were identified.These included a history of chronic obstructive pulmonary disease with a regression coefficient(Coef)of 2.54(P<0.001),length of stay(Coef 0.08,P<0.001),prior deep venous thrombosis(Coef 1.61,P<0.001),hemoglobin A1c>7%(Coef 1.19,P<0.001),venous stasis history(Coef 1.43,P<0.001),and preoperative anticoagulation use(Coef 1.24,P<0.001).These variables were weighted according to their regression coefficients in an algorithm that was generated for the model predicting 30-d VTE risk post-RYGB.The risk model's area under the curve(AUC)was 0.79[95%confidence interval(CI):0.63-0.81],showing good discriminatory power,achieving a sensitivity of 0.60 and a specificity of 0.91.Without training,the same model performed satisfactorily in patients with laparoscopic sleeve gastrectomy with an AUC of 0.63(95%CI:0.62-0.64)and endoscopic sleeve gastroplasty with an AUC of 0.76(95%CI:0.75-0.78).CONCLUSION This simple risk model uses only six variables to assist clinicians in the preoperative risk stratification of RYGB patients,offering insights into factors that heighten the risk of VTE events.展开更多
The development of the heart-lung machine made repair of intracardiac lesions possible. One of the key requirements of the heart-lung machine was anticoagulation. Heparin was discovered by a medical student, Jay McLea...The development of the heart-lung machine made repair of intracardiac lesions possible. One of the key requirements of the heart-lung machine was anticoagulation. Heparin was discovered by a medical student, Jay McLean, working in the laboratory of Dr. William Howell at Johns Hopkins. John Gibbon contributed more to the successful development of the heart-lung machine than anyone else. His interest began as a young doctor since 1930s. Gibbon's work on the heart-lung machine took place over the next 20 years in laboratories at Massachusetts General Hospital, the University of Pennsylvania, and Thomas Jefferson University. In 1937, he reported the first successful demonstration that life could be maintained by an artificial heart and lung, and the native heart and lungs could resume function. After World War Ⅱ, Dr. Gibbon resumed his work and received support from IBM to build a heart-lung machine on a more sophisticated scale. Eventually, the team developed a larger oxygenator that the IBM engineers incorporated into a new machine. On May 6, 1953, Dr. Gibbon performed the first successful operation using an extracorporeal circuit on an 18-year-old girl with a large atrial septa1 defect. It wasn't until 1958, when a system that involved bubbling blood was perfected, that "heart-lung" machines came of age. Despite so many chill winds and cold rains, "heart-lung" machine, the budding rose of surgery, was eventually blossom brightly in the radiant rays of sunlight. John Gibbon's dream had become a reality. His work serves as an important example to surgeons who are struggling today with the surgical therapies and technologies of tomorrow.展开更多
Objective: This case report aimed to highlight intersections of TB and Cardiovasular diseases which carry high morbidity and mortality rates. Methods: We are reporting the surgical management of forty seven years fema...Objective: This case report aimed to highlight intersections of TB and Cardiovasular diseases which carry high morbidity and mortality rates. Methods: We are reporting the surgical management of forty seven years female who had back ground history of IDDM (Insulin dependent diabetic Mellitus), ESRD (End stage renal disease) on HD (haemodialysis) also she had left subclavian artery stenosis, and paroxysmal atrial fibrillation. She was diagnosed with mitral valve infective endocarditis and found accidently to have an open pulmonary tuberculosis (TB) on the day before surgery. Results: She was started on first line anti-TB treatment. She was isolated in her private room and airborne precautions measures applied. The patient underwent a tissue Mitral Valve replacement and tricuspid valve repair annuloplasty. Special precautions were applied in Theatre and on cardiopulmonary bypass Machine guided by KAMC-J disinfection protocol. The patient made good recovery postoperatively. She was discharged well on day 7 post operatively. Conclusion: Intersections of TB and cardiovasular diseases carry high morbidity and mortality rates. Early diagnosis and early anti tuberculosis treatment can surely improve the patient prognosis. Our decontamination and disinfective procedures are recommended. Cases like this should be monitored long term for the development of further cardiovascular complication.展开更多
(Aim)The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022.Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients.(Method)Two datasets are ch...(Aim)The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022.Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients.(Method)Two datasets are chosen for this study.The multiple-way data augmentation,including speckle noise,random translation,scaling,salt-and-pepper noise,vertical shear,Gamma correction,rotation,Gaussian noise,and horizontal shear,is harnessed to increase the size of the training set.Then,the SqueezeNet(SN)with complex bypass is used to generate SN features.Finally,the extreme learning machine(ELM)is used to serve as the classifier due to its simplicity of usage,quick learning speed,and great generalization performances.The number of hidden neurons in ELM is set to 2000.Ten runs of 10-fold cross-validation are implemented to generate impartial results.(Result)For the 296-image dataset,our SNELM model attains a sensitivity of 96.35±1.50%,a specificity of 96.08±1.05%,a precision of 96.10±1.00%,and an accuracy of 96.22±0.94%.For the 640-image dataset,the SNELM attains a sensitivity of 96.00±1.25%,a specificity of 96.28±1.16%,a precision of 96.28±1.13%,and an accuracy of 96.14±0.96%.(Conclusion)The proposed SNELM model is successful in diagnosing COVID-19.The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.展开更多
文摘BACKGROUND Roux-en-Y gastric bypass(RYGB)is a widely recognized bariatric procedure that is particularly beneficial for patients with class III obesity.It aids in significant weight loss and improves obesity-related medical conditions.Despite its effectiveness,postoperative care still has challenges.Clinical evidence shows that venous thromboembolism(VTE)is a leading cause of 30-d morbidity and mortality after RYGB.Therefore,a clear unmet need exists for a tailored risk assessment tool for VTE in RYGB candidates.AIM To develop and internally validate a scoring system determining the individualized risk of 30-d VTE in patients undergoing RYGB.METHODS Using the 2016–2021 Metabolic and Bariatric Surgery Accreditation Quality Improvement Program,data from 6526 patients(body mass index≥40 kg/m^(2))who underwent RYGB were analyzed.A backward elimination multivariate analysis identified predictors of VTE characterized by pulmonary embolism and/or deep venous thrombosis within 30 d of RYGB.The resultant risk scores were derived from the coefficients of statistically significant variables.The performance of the model was evaluated using receiver operating curves through 5-fold cross-validation.RESULTS Of the 26 initial variables,six predictors were identified.These included a history of chronic obstructive pulmonary disease with a regression coefficient(Coef)of 2.54(P<0.001),length of stay(Coef 0.08,P<0.001),prior deep venous thrombosis(Coef 1.61,P<0.001),hemoglobin A1c>7%(Coef 1.19,P<0.001),venous stasis history(Coef 1.43,P<0.001),and preoperative anticoagulation use(Coef 1.24,P<0.001).These variables were weighted according to their regression coefficients in an algorithm that was generated for the model predicting 30-d VTE risk post-RYGB.The risk model's area under the curve(AUC)was 0.79[95%confidence interval(CI):0.63-0.81],showing good discriminatory power,achieving a sensitivity of 0.60 and a specificity of 0.91.Without training,the same model performed satisfactorily in patients with laparoscopic sleeve gastrectomy with an AUC of 0.63(95%CI:0.62-0.64)and endoscopic sleeve gastroplasty with an AUC of 0.76(95%CI:0.75-0.78).CONCLUSION This simple risk model uses only six variables to assist clinicians in the preoperative risk stratification of RYGB patients,offering insights into factors that heighten the risk of VTE events.
文摘The development of the heart-lung machine made repair of intracardiac lesions possible. One of the key requirements of the heart-lung machine was anticoagulation. Heparin was discovered by a medical student, Jay McLean, working in the laboratory of Dr. William Howell at Johns Hopkins. John Gibbon contributed more to the successful development of the heart-lung machine than anyone else. His interest began as a young doctor since 1930s. Gibbon's work on the heart-lung machine took place over the next 20 years in laboratories at Massachusetts General Hospital, the University of Pennsylvania, and Thomas Jefferson University. In 1937, he reported the first successful demonstration that life could be maintained by an artificial heart and lung, and the native heart and lungs could resume function. After World War Ⅱ, Dr. Gibbon resumed his work and received support from IBM to build a heart-lung machine on a more sophisticated scale. Eventually, the team developed a larger oxygenator that the IBM engineers incorporated into a new machine. On May 6, 1953, Dr. Gibbon performed the first successful operation using an extracorporeal circuit on an 18-year-old girl with a large atrial septa1 defect. It wasn't until 1958, when a system that involved bubbling blood was perfected, that "heart-lung" machines came of age. Despite so many chill winds and cold rains, "heart-lung" machine, the budding rose of surgery, was eventually blossom brightly in the radiant rays of sunlight. John Gibbon's dream had become a reality. His work serves as an important example to surgeons who are struggling today with the surgical therapies and technologies of tomorrow.
文摘Objective: This case report aimed to highlight intersections of TB and Cardiovasular diseases which carry high morbidity and mortality rates. Methods: We are reporting the surgical management of forty seven years female who had back ground history of IDDM (Insulin dependent diabetic Mellitus), ESRD (End stage renal disease) on HD (haemodialysis) also she had left subclavian artery stenosis, and paroxysmal atrial fibrillation. She was diagnosed with mitral valve infective endocarditis and found accidently to have an open pulmonary tuberculosis (TB) on the day before surgery. Results: She was started on first line anti-TB treatment. She was isolated in her private room and airborne precautions measures applied. The patient underwent a tissue Mitral Valve replacement and tricuspid valve repair annuloplasty. Special precautions were applied in Theatre and on cardiopulmonary bypass Machine guided by KAMC-J disinfection protocol. The patient made good recovery postoperatively. She was discharged well on day 7 post operatively. Conclusion: Intersections of TB and cardiovasular diseases carry high morbidity and mortality rates. Early diagnosis and early anti tuberculosis treatment can surely improve the patient prognosis. Our decontamination and disinfective procedures are recommended. Cases like this should be monitored long term for the development of further cardiovascular complication.
基金This paper is partially supported by Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)Royal Society International Exchanges Cost Share Award,UK(RP202G0230)+5 种基金British Heart Foundation Accelerator Award,UK(AA/18/3/34220)Hope Foundation for Cancer Research,UK(RM60G0680)Global Challenges Research Fund(GCRF),UK(P202PF11)Sino-UK Industrial Fund,UK(RP202G0289)LIAS Pioneering Partnerships award,UK(P202ED10)Data Science Enhancement Fund,UK(P202RE237).
文摘(Aim)The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022.Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients.(Method)Two datasets are chosen for this study.The multiple-way data augmentation,including speckle noise,random translation,scaling,salt-and-pepper noise,vertical shear,Gamma correction,rotation,Gaussian noise,and horizontal shear,is harnessed to increase the size of the training set.Then,the SqueezeNet(SN)with complex bypass is used to generate SN features.Finally,the extreme learning machine(ELM)is used to serve as the classifier due to its simplicity of usage,quick learning speed,and great generalization performances.The number of hidden neurons in ELM is set to 2000.Ten runs of 10-fold cross-validation are implemented to generate impartial results.(Result)For the 296-image dataset,our SNELM model attains a sensitivity of 96.35±1.50%,a specificity of 96.08±1.05%,a precision of 96.10±1.00%,and an accuracy of 96.22±0.94%.For the 640-image dataset,the SNELM attains a sensitivity of 96.00±1.25%,a specificity of 96.28±1.16%,a precision of 96.28±1.13%,and an accuracy of 96.14±0.96%.(Conclusion)The proposed SNELM model is successful in diagnosing COVID-19.The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.
