Complex Pyogenic Liver Abscess: Outcome of Open vs Laparoscopic Drainage

OBJECTIVES Our study aimed to evaluate the safety and efficacy of laparoscopic drainage as a management of complex pyogenic liver abscesses in comparison to open surgical drainage. METHODOLOGY The comparative research design was used to compare the outcomes, complications, perioperative morbidity, mortality, and potential recurrence of 60 patients with a complex pyogenic liver abscess who were hospitalized at the General Surgery Department of Hayatabad Medical Complex Peshawar and treated either laparoscopically or openly from January 2019 to December 2020. 30 patients had open drainage management, while 30 patients received laparoscopic drainage management. For all patients, pus was examined for culture sensitivity. Patients with a small, solitary and unilocular pyogenic liver abscess that improved with antibiotic therapy and or/and percutaneous drainage were excluded. Each patient had a thorough clinical evaluation, lab tests, ultrasound, computed tomography, or magnetic resonance imaging of the pelvis and abdomen. RESULTS All patients underwent abdominal ultrasonography & sonographic diagnosis was made in 43(71.7%), followed by a computed tomography scan (CT) in 12(20%) & magnetic resonance imaging (MRI) diagnosis was made in 5(8.3%) patients respectively. Diabetes mellitus was present in 15(25%) patients, severe chronic obstructive pulmonary disease in 10(16.7%) and severe anemia in 9(15%) patients. All individuals associated with co-morbidity were considered high-risk patients. CONCLUSION Laparoscopic drainage of liver abscess has a shorter surgical time, lower morbidity rate, and shorter hospital stay as compared to open surgical drainage

Adherence to treatment: Doctor vs patient perspective

It has been demonstrated over time that patients with haemoglobinopathies who exhibit a high level of compliance to proper therapy benefit not only from higher life expectancy but also from significantly better quality of life. The treatment of thalassaemia consists of blood transfusions and iron chelation therapy. Managing any complications due to iron overload, performing all necessary clinical and laboratory examinations and dealing effectively with psychological issues are also very important. Blood transfusion scheme must be designed by the treating physician according to the patient’s clinical needs. Chelation therapy should be aimed at selecting the right medication and the right dose. Examinations should be as organized as possible, and the management of complications depends significantly on cooperation with experienced specialists in each respective field. Ultimately, effectiveness of treatment and patient’s psychological well-being (acceptance of the disease and positive attitude) are the most decisive factors, as they seem to be connected to adherence through a mechanism of positive feedback. Hence, professional psychological support should be part of multidisciplinary care. Difference of point of view between doctor and patient can often be the reason behind misinterpretations or misunderstandings

Interaoperative frozen section consultation: an analysis of accuracy in a teaching hospital.

This Is a retrospective quality assurance study of all frozen sections done at The Aga Khan University Hospital during a six year period (1986 to 1991). There were 1,031 frozen sections out of a cumulative total of 42,985 surgical specimens (2.39%). Nine hundred and seventy-six (94.66%) were concordant. In 92(8.9%) fresh specimens were brought from other hospitals of Karachi, in 37 cases (3.58%) the diagnosis was deferred till the evaluation of permanent paraffin sections and 18 (1,74%) were discordant with 7 (0.67%) false positive and 11(1,06%) false negative. Among the discordant cases, 9 were attributed to misinterpretation, 7 due to sampling errors and 2 due to technical reasons. Some of these errors might have been avoided, but appear to be an Irreducible minimum

Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods

Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping patients reverse diabetes from the early stage to live healthy lives. Machine learning (ML) is a useful tool that can easily detect diabetes from several risk factors and, based on the findings, provide a decision-based model that can help in diagnosing the disease. This study aims to detect the risk factors of diabetes using ML methods and to provide a decision support system for medical practitioners that can help them in diagnosing diabetes. Moreover, besides various other preprocessing steps, this study has used the synthetic minority over-sampling technique integrated with the edited nearest neighbor (SMOTE-ENN) method for balancing the BRFSS dataset. The SMOTE-ENN is a more powerful method than the individual SMOTE method. Several ML methods were applied to the processed BRFSS dataset and built prediction models for detecting the risk factors that can help in diagnosing diabetes patients in the early stage. The prediction models were evaluated using various measures that show the high performance of the models. The experimental results show the reliability of the proposed models, demonstrating that k-nearest neighbor (KNN) outperformed other methods with an accuracy of 98.38%, sensitivity, specificity, and ROC/AUC score of 98%. Moreover, compared with the existing state-of-the-art methods, the results confirm the efficacy of the proposed models in terms of accuracy and other evaluation measures. The use of SMOTE-ENN is more beneficial for balancing the dataset to build more accurate prediction models. This was the main reason it was possible to achieve models more accurate than the existing ones

Withering timings affect the total free amino acids and mineral contents of tea leaves during black tea manufacturing

AbstractThe aim of the present study was to investigate the effect of withering timings (i.e. 0, 21, 22, 23 and 24h) on the moisture, total free amino acids, ash, essential and toxic mineral element contents of tea (Camellia sinensis L.) leaves during black tea manufacturing. Moisture, ash, Na, P, Mg, Cu, Zn, Mn, Al, Ni and Pb contents were significantly (P<0.05) affected by withering, whereas non-significant (P>0.05) results were noted for total free amino acids, K, Fe and Cd contents. The highest moisture content (76.4%) was examined in fresh leaves that progressively decreased to 63.8% in 24h withering. Total free amino acid contents gradually increased up to 23h and then decreased. Ash, P, Cu, Zn and Mn contents showed an increasing trend with withering time. Conversely, significantly lowered amounts of Na (162.5mg/kg) and Mg (803mg/kg) were recorded in tea leaves after 24h withering. Among the toxic elements, Al, Ni and Pb contents were progressively increased over withering time. It was concluded that tea is a potential source of essential chemical constituents and during processing proper care should be taken to produce high quality black tea