BACKGROUND The root of mesentery dissection is one of the critical maneuvers,especially in borderline resectable pancreatic head cancer.Intra-abdominal chyle leak(CL)including chylous ascites may ensue in up to 10%of patients after pancreatic resections.Globally recognized superior mesenteric artery(SMA)first approaches are invariably performed.The mesenteric dissection through the inferior infracolic approach has been discussed in this study emphasizing its post-operative impact on CL which is the cornerstone of this study.AIM To assess incidence,risk factors,clinical impact of CL following root of mesentery dissection,and the different treatment modalities.METHODS This is a retrospective study incorporating the patients who underwent dissection of the root of mesentery with inferior infracolic SMA first approach pancreat-oduodenectomy for the ventral body and uncinate mass of pancreas in the Department of Gastrointestinal and General Surgery of Kathmandu Medical College and Teaching Hospital from January 1,2021 to February 28,2024.Intraop-erative findings and postoperative outcomes were analyzed.RESULTS In three years,ten patients underwent root of mesentery dissection with inferior infracolic SMA first approach pancreatoduodenectomy.The mean age was 67.6 years with a male-to-female ratio of 4:5.CL was seen in four patients.With virtue of CL,Clavien-Dindo grade Ⅱ or higher morbidity was observed in four patients.Two patients had a hospital stay of more than 20 days with the former having a delayed gastric emptying and the latter with long-term total parenteral nutrition requirement.The mean operative time was 330 minutes.Curative resection was achieved in 100%of the patients.The mean duration of the intensive care unit and hospital stay were 2.55±1.45 days and 15.7±5.32 days,respectively.CONCLUSION Root of mesentery dissection with lymphadenectomy and vascular resection correlated with occurrence of CL.After complete curative resection,these were managed with total parenteral nutrition without adversely imp
Prabir MaharjanSujan RegmeeSpandan D AdhikariRabin PahariRoshan GhimireDhiresh K MaharjanSuman K ShresthaPrabin B Thapa
Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in cl
Background:Insomnia is a prevalent clinical condition and Shangxia Liangji formula(SXLJF)is a well-established method of treatment.Nevertheless,the specific mechanism of action of SXLJF remains unclear.Methods:The mouse model of insomnia was established by intraperitoneal injection of para-chlorophenylalanine.Forty-two mice were randomly divided into a negative control group,model group,SXLJF group(18.72 g/kg/day),and positive control group(diazepam,2 mg/kg)and treated with the corresponding drugs for 7 consecutive days.The open field test and pentobarbital-induced sleeping test were conducted.LC-MS-based untargeted metabolomics and network pharmacology were applied to explore the potential targets of SXLJF for treating insomnia.Finally,key targets were validated using RT-qPCR.Results:Behavioral tests demonstrated that SXLJF reduced the total distance,average velocity,central distance,and sleep latency,and prolonged sleep duration.Metabolomics and network pharmacology revealed potential targets,signaling pathways,metabolic pathways,and metabolites associated with the anti-insomnia effects of SXLJF.Specifically,tyrosine hydroxylase(TH)and tyrosine metabolism emerged as crucial metabolic pathways and targets,respectively.RT-qPCR results supported the role of TH in the mechanism of SXLJF in treating insomnia.Conclusion:In conclusion,TH and tyrosine metabolism may represent significant targets and pathways for SXLJF in treating insomnia.
Background:Non-alcoholic fatty liver disease(NAFLD)is a liver disorder characterized by the accumulation and degeneration of fat in the liver cells,a condition that may further deteriorate and lead to cirrhosis and liver cancer.Numerous studies showed that metabolic dysfunction can promote NAFLD development.Linggui Zhugan Decoction(LGZGD)has therapeutic effects on NAFLD.The mechanism of LGZGD still remains unclear.This study was to examine the impact of LGZGD on the metabolic processes involved in the development of NAFLD.Methods:A mice model of NAFLD was treated with LGZGD.The therapeutic potential of LGZGD was evaluated by assessing the activity of transaminases,lipids levels of blood,and pathological changes in the liver of the mice model of NAFLD.Additionally,this study also evaluated the influence of LGZGD on liver inflammation and oxidative stress.Results:The results of untargeted metabolomics analysis showed that LGZGD reduced the disordered lipid metabolism in NAFLD mice.LGZGD improved the oxidative stress and also reduced the levels of pro-inflammatory cytokines in the liver.Untargeted metabolomics analysis of liver samples revealed that LGZGD treatment improved metabolic disorders,including alanine,aspartate,glutamate,glycerophospholipid metabolism,and citrate cycle.Further RT-qPCR and Western blot results showed that LGZGD could regulate the expression of key enzymes in the metabolic pathway of the citrate cycle,including ATP-citrate lyase(ACLY),alanine-glyoxylate aminotransferase-2(AGXT2),phosphatidylethanolamine N-methyltransferase(PEMT),and succinate dehydrogenase(SDH).Conclusion:We found that LGZGD can treat NAFLD by reducing inflammatory responses,inhibiting oxidative stress,regulating alanine,aspartate,glutamate,and glycerophospholipid metabolism,and citrate cycle pathways.
This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.
Ghaliah AlhamziBadr Saad TAlkahtaniRavi Shanker DubeyMati ur Rahman