Assessment of distress in patients visiting pain and palliative out patient department of a tertiary care hospital in Assam

Background: Distress is a multifactorial entity seen in patients battling with cancer. It is under-reported and under-managed posing a hurdle in providing holistic patient care. It has a negative effect on the treatment and treatment adherence in cancer patients.Methods:  A cross- sectional study was done in the pain and palliative outpatient clinic of State Cancer Institute, Guwahati, Assam. About 250 patients were screened for distress using a questionnaire consisting of national comprehensive cancer network (NCCN) distress thermometer (DT) and problem list over a span of 4 months after obtaining due consent. They were asked to mark their level of distress and the contributing factors for distress as enlisted in the NCCN DT problem list. Chi square test and Mann Whitney test were used to analyze statistical significance.Results: The distress levels were assessed based on the effects of the disease, treatment and other demographic variables and possible causes contributing to distress were determined. It was noted that out of 250 patients screened (n=250), 88 (35.2%) reported a distress score of 4 while 79 (31.6%) reported with a score of 6. Nature of the disease and current treatment received by the patient was significantly associated with distress while age, marital status and sex was not. Amongst physical, emotional and practical problems leading to distress, pain (45.2%), worry (49.2%) and transportation issues (45.1%) respectively were significantly reported as primary causes of distress.Conclusions: NCCN DT in an effective tool for assessment of distress in cancer patients of Assam. It helps in identification of probable causes of distress which aids in holistic patient care.

Compliance, efficacy and quality of life for oral morphine versus transdermal fentanyl patch in management of cancer pain

Background: A randomized, open, two-period, crossover study was done on cancer patients requiring strong opioid analgesia (n=104, mean age 63.5, range18-83 years) recruited from State Cancer Institute, Gauhati Medical College and Hospital, comparing transdermal fentanyl with oral morphine.Methods: Patients received one treatment for 15 days followed immediately by the other for 15 days.Results: Transdermal fentanyl provided good pain relief and it was also was associated with less constipation when compared to oral morphine(p<0.05). Of those who were able to express a preference, significantly more preferred fentanyl patches.Conclusions: Transdermal fentanyl patch provided good pain relief, equivalent to that provided by oral morphine, required lesser rescue doses, improved quality of life and is associated with less constipation when compared to morphine, and was preferred more by patients.

Piezoelectric Energy Generation and Harvesting at the Nano-Scale: Materials and Devices

Continuous efforts are being made for alternative power generation techniques for use in devices with sub-micron-scale dimensions. In this work, we have perused through some of these efforts, focusing mainly on devices using materials with piezoelectric properties. In the last decade or so, nanostructured zinc oxide (ZnO) has drawn worldwide attention for its path-breaking properties. One of its most extensively-studied properties is its ability to generate power when subjected to mechanical vibration. It can generate a potential within a wide range of frequency vibrations, from a few Hz to thousands of KHz. This can lead to extensive application prospects in many important fields, like self-power generating devices for medical applications, wireless technologies and various sensors, etc. This review looks into reports related to such technology in detail. It also takes into account certain other materials that have been reported for piezoelectric energy scavenging applications

Diffusion Study of Electrodeposited Copper-Nickel Multilayer

Copper and Nickel multilayer composites have been prepared by electrodeposition of nickel on copper substrate from Watt’s bath, producing Ni layer of low thickness and then subsequent deposition of copper on Ni by acidic sulfate bath, leading to electroplating of a thin Cu layer. Since adherent Nickel deposit cannot be deposited directly from conventional baths, copper substrate serves as an ideal undercoat for obtaining adherent Ni layer deposit with improved metal distribution and reduced critical cleaning & polishing requirements. Plating parameters such as current density and plating time are kept constant whereas the other bath compositions differ. This is done alternatively to generate alternate Cu-Ni metal multilayer. Magnetic stirring was applied during deposition process in order to produce homogeneous, smooth and adherent coatings of nickel. Characterization and surface morphology analysis is done using FESEM and optical imaging and thus, the thickness of the layers is obtained. The Cu-Ni multilayered composite is then subjected to vacuum heating so as to obtain the Time Temperature profile and the extent of interlayer diffusion. This vacuum furnace heating was carried out in the presence of Argon gas, at different temperatures (773K, 973K, 1173K) and the samples are held at these temperatures each for different time intervals (1 hr, 2hrs, 3hrs). The resultant hardness, microstructure and the diffusion effects are then investigated and studied. The diffusion coefficients for Cu in Ni and Ni in Cu at the above mentioned temperatures was studied from theoretical calculation and subsequently compared with actual diffusion coefficients measured from the Energy Dispersive Spectroscopy (EDS) line scan

Lipid profile in systemic lupus erythematosus: study from a tertiary teaching hospital of Eastern India

Background: Dyslipidemia is an independent modifiable risk factor for coronary artery disease. Patients with systemic lupus erythematosus (SLE) have dyslipidemia and accelerated atherosclerosis; however, there is paucity of published data on the lipid profile in patients with SLE in Eastern India. This study was done to assess the prevalence and abnormality of lipid profile in patients with SLE admitted to a tertiary care teaching hospital in Eastern India.Methods: This was a hospital based prospective study evaluating SLE patients admitted to a tertiary care institution in Eastern India. 101 patients with SLE admitted consecutively and 100 age and sex matched controls were enrolled for study. Fasting total cholesterol (TC), triglycerides (TG), high density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured in plasma whereas very low-density lipoprotein cholesterol (VLDL-C) was calculated. Statistical analysis was done using the standard statistical techniques.Results: Out of 101 patients of SLE, 97 were female and 4 were of male gender. The age of the patients ranged from 15 to 47 years with a mean of 27.17 (±8.4) years. Dyslipidemia was found in 58(57.4%) patients. Hypercholesterolemia was found in 23 (22.7%), hypertriglyceridemia in 55 (54.4%), raised LDL-C in 24 (23.7%) cases. Raised TC, TG, and LDL-C was found in 18 (17.8%), and raised TC, TG, LDL-C and low HDL-C was found in 9 (8.9%) cases. There was significant increase in serum cholesterol, triglyceride and VLDL-C while decrease in HDL-C in SLE patients than controls (p <0.001). Statistically no difference in lipid profile was found in between groups of SLE receiving steroid and without steroid.Conclusions: Abnormal lipid profiles are very common in patients with SLE, though the patients are very young. Control of dyslipidemia can favourably affect cardiovascular related morbidity and mortality. 

Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations

There is no denying how machine learning and computer vision have grown in the recent years. Their highest advantages lie within their automation, suitability, and ability to generate astounding results in a matter of seconds in a reproducible manner. This is aided by the ubiquitous advancements reached in the computing capabilities of current graphical processing units and the highly efficient implementation of such techniques. Hence, in this paper, we survey the key studies that are published between 2014 and 2020, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic-tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and further partitioned if the amount of works that fall under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites, containing masks of the aforementioned tissues, are thoroughly discussed, highlighting the organizers original contributions, and those of other researchers. Also, the metrics that are used excessively in literature are mentioned in our review stressing their relevancy to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing such as the scarcity of many studies on the vessels segmentation challenge, and why their absence needs to be dealt with in an accelerated manner.Comment: 41 pages, 4 figures, 13 equations, 1 table. A review paper on liver tissues segmentation based on automated ML-based technique

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Machine learning and computer vision techniques have grown rapidly in recent years due to their automation, suitability, and ability to generate astounding results. Hence, in this paper, we survey the key studies that are published between 2014 and 2022, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and they are further partitioned if the amount of work that falls under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites containing masks of the aforementioned tissues are thoroughly discussed, highlighting the organizers' original contributions and those of other researchers. Also, the metrics used excessively in the literature are mentioned in our review, stressing their relevance to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing, such as the scarcity of many studies on the vessels' segmentation challenge and why their absence needs to be dealt with sooner than later. 2022 The Author(s)This publication was made possible by an Award [GSRA6-2-0521-19034] from Qatar National Research Fund (a member of Qatar Foundation). The contents herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National LibraryScopu