Outcome of split liver transplantation vs living donor liver transplantation:A systematic review and meta-analysis

BACKGROUND The outcomes of liver transplantation(LT)from different grafts have been studied individually and in combination,but the reports were conflicting with some researchers finding no difference in both short-term and long-term outcomes between the deceased donor split LT(DD-SLT)and living donor LT(LDLT).AIM To compare the outcomes of DD-SLT and LDLT we performed this systematic review and meta-analysis.METHODS This systematic review was performed in compliance with the Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines.The following databases were searched for articles comparing outcomes of DD-SLT and LDLT:PubMed;Google Scholar;Embase;Cochrane Central Register of Controlled Trials;the Cochrane Database of Systematic Reviews;and Reference Citation Analysis(https://www.referencecitationanalysis.com/).The search terms used were:“liver transplantation;”“liver transplant;”“split liver transplant;”“living donor liver transplant;”“partial liver transplant;”“partial liver graft;”“ex vivo splitting;”and“in vivo splitting.”RESULTS Ten studies were included for the data synthesis and meta-analysis.There were a total of 4836 patients.The overall survival rate at 1 year,3 years and 5 years was superior in patients that received LDLT compared to DD-SLT.At 1 year,the hazard ratios was 1.44(95%confidence interval:1.16-1.78;P=0.001).The graft survival rate at 3 years and 5 years was superior in the LDLT group(3 year hazard ratio:1.28;95%confidence interval:1.01-1.63;P=0.04).CONCLUSION This meta-analysis showed that LDLT has better graft survival and overall survival when compared to DD-SLT.

Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs.Magnetic Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs tissue.However,the interpretation of medical images requires the subjective expertise of a radiologist and oncologist.Thus,building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses.This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images.This paper proposed two models;the first one employed the 3D features while the second exploited the 2D features.The first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories.On top of that,the proposed method is applied to 2D slices for comparison purposes.The proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver tumors.On the other hand,the performance is lower in 2D classification.The maximum accuracy reached 96.4%for two classes and 92.1%for four classes.The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes.The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D classification.In the future,the presented work can be extended to be used in the huge dataset.Then,it can be a reliable,efficient Computer Aided Diagnosis(CAD)system employed in hospitals in rural areas.

Clinical presentation,management,screening and surveillance for colorectal cancer during the COVID-19 pandemic

Management of colorectal cancer(CRC)was severely affected by the changes implemented during the pandemic,and this resulted in delayed elective presentation,increased emergency presentation,reduced screening and delayed definitive therapy.This review was conducted to analyze the impact of the coronavirus disease 2019(COVID-19)pandemic on management of CRC and to identify the changes made in order to adapt to the pandemic.We performed a literature search in PubMed,Medline,Index Medicus,EMBASE,SCOPUS,Reference Citation Analysis(https://www.referencecitationanalysis.com/)and Google Scholar using the following keywords in various combinations:Colorectal cancer,elective surgery,emergency surgery,stage upgrading,screening,surveillance and the COVID-19 pandemic.Only studies published in English were included.To curtail the spread of COVID-19 infection,there were modifications made in the management of CRC.Screening was limited to high risk individuals,and the screening tests of choice during the pandemic were fecal occult blood test,fecal immunochemical test and stool DNA testing.The use of capsule colonoscopy and open access colonoscopy was also encouraged.Blood-based tests like serum methylated septin 9 were also encouraged for screening of CRC during the pandemic.The presentation of CRC was also affected by the pandemic with more patients presenting with emergencies like obstruction and perforation.Stage migration was also observed during the pandemic with more patients presenting with more advanced tumors.The operative therapy of CRC was altered by the pandemic as more emergencies surgeries were done,which may require exteriorization by stoma.This was to reduce the morbidity associated with anastomosis and encourage early discharge from the hospital.There was also an initial reduction in laparoscopic surgical procedures due to the fear of aerosols and COVID-19 infection.As we gradually come out of the pandemic,we should remember the lessons learned and continue to apply them even after the pandemic passes.

Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

Cervical cancer is screened by pap smear methodology for detection and classification purposes.Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues.In this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes problem.Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells.Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)classifier.The success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%sensitivity.On top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high accuracy.On the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%accuracy.The proposed system is a built-in cascading manner with five models of polynomial(SVM)classifier.The overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

Biphasic Insulin Aspart 30 Therapy in Insulin-Naïve and Insulin-Experienced Patients with Type 2 Diabetes: Results from the Jordanian Subgroup of the A₁chieve Study

Objective: To analyse the safety and effectiveness of biphasic insulin aspart 30 (BIAsp 30) in a Jordanian subgroup of the 24-week, non-interventional A1chieve study. Methods: A total of 509 Jordanian patients with type 2 diabetes (392 insulin-naive and 117 insulin-experienced) starting BIAsp30, alone or in combination with oral glucose-lowering drugs, were included. Safety and effectiveness outcomes were analysed over 24 weeks. Results: Patients had a mean age of 55.8 years, body mass index of 28.8 kg/m2 and diabetes duration of 9.4 years at baseline. Two serious adverse drug reactions of hypoglycaemia were reported. The proportion of patients who reported major hypoglycaemic events decreased (2.4% at baseline vs. 0.2% at Week 24, p = 0.0039). The proportion of patients reporting overall hypoglycaemia increased marginally (6.3% at baseline vs. 9.9% at Week 24, p = 0.0378), primarily attributed to a rise in minor and nocturnal hypoglycaemia reported in insulin-naive patients. From baseline to Week 24, the mean ± SD glycated haemoglobin A1c level decreased from 9.8% ± 1.4% to 7.4% ± 0.9% (p < 0.001). Significant reductions after 24 weeks were also noted in the mean fasting plasma glucose, postprandial plasma glucose, lipids, systolic blood pressure and quality of life (all p < 0.001), while the mean body weight increased by 1.8 ± 6.5 kg (p < 0.001). Conclusion: Overall, BIAsp 30 therapy was well-tolerated and resulted in improved glycaemic control in this Jordanian subgroup over 24 weeks.