Academic literature on the topic 'Clinical pharmacist plays a major role in detecting'

Abstract: Spiranolactone and Eplerenone were belongs to the class of potassium sparing diuretics where as Torsemide belongs to the loop diuretic class, they mainly acts by blocking the function of aldosterone hormone to retain the sodium and excrete potassium. By irrational use of these drugs induced dyselectrolytemia like hyperkalemia and hyponatremia. A 55 years female patient was admitted in cardiology department with the chief complaints of drowsiness and slow response to commands by using of the diuretics (Spiranolactone , Eplerenone and Torsemide) causes the electrolytes induced hyperkalemia and hyponatremia. These electrolytes abnormalities will causes the cardiac arrhythmias , muscle paralysis and sometimes death also. Whereas both rechallenge and dechallenge was done and the ADR assessment scales like Naranjo and WHO gives certain ADR and it can be managed by stopping of the above drugs. So, clinical pharmacist plays a major role in detecting, monitoring and managing of ADRs.

The rise of technology in healthcare has led to dramatic changes in approaches to patient care by healthcare professionals. The realm of digital health has created new opportunities for pharmacists to engage patients in clinical practice. Pharmacies and industry are increasingly integrating these innovations into their businesses and practice. This article highlights areas of digital health for pharmacists to be aware of, in particular regarding areas of medication adherence and disease management. Technology plays a massive role in our individual lives; it has morphed the human experience in ways that were simply unimaginable 50 years ago. We use technology in nearly every facet of our lives. From detecting an appropriate intensity with which to brush our teeth to counting calories lost through the course of a day, technology has made a major impact on individual health. The integration of technology into our everyday lives has changed the way we communicate, how we capture and share our lives with others, how we seek answers, and how we experience life overall. Given this change in the way people operate, it is important that pharmacists adapt to these trends and incorporate technology into daily practice. The incorporation of mobile devices and technology into healthcare has been coined as mobile health (mHealth), which falls under the broader spectrum of digital health.1 –4 Digital health focuses on the integration of mobile tools (e.g., smartphones), wearable devices, and telehealth to help personalize the treatment of patients through the widespread adoption of wireless technology. The idea of involving pharmacists in mHealth has been a topic of recent interest, due in large part to the potential ramifications for the profession.4 Today, patients are using the Internet to research their health questions and help guide their personal health choices, and some of the information they find can be misleading and unreliable. It is of the utmost importance that healthcare professionals ensure there are credible sources for patients to research their questions. As pharmacists, we can research and recommend tools to patients to help solve problems related to drug information, medication adherence, and access, which includes the recent rise of novel technological devices. All of our patients will have different comfort levels with technology; despite this spectrum, there is a place for everyone to feel comfortable using digital health tools. However, there are recent technological advances coming to the field, which are already providing a benefit to patients, ranging from mobile applications to wearable technologies to ingestible medications that notify providers of patient medication adherence. We seek to help pharmacists understand the different areas of digital health, which may have substantial influence on the realm of pharmacy practice in the years to come by addressing current and upcoming digital health developments.

Filariasis is a major public health problem in tropical countries, including India. Fine needle aspiration cytology plays an important role in prompt recognition of disease. Aim was to highlight the role of fine needle aspiration cytology as a simple and cost effective tool to detect microfilarial infestation. A retrospective study of 10 cases in which fine needle aspiration cytology was done and was useful in detecting microfilaria. Patient’s age were ranging from 19-62 years. M:F ratio being 7:3. Out of ten cases, maximum cases of microfilarial detection was reported in soft tissue swelling (four cases), followed by lymph node swelling (three cases), thyroid swelling (two cases) and breast swelling (one case). Careful screening of fine needle aspiration cytology smears is helpful in detecting microfilaria even in asymptomatic patients living in endemic zone which plays a significant role in recognition of disease and obviating severe manifestations of filariasis if treated in time.

Since its inception, the field of parenteral nutrition has continued to evolve requiring the expertise of several health care disciplines. This feature has made nutrition support unique among clinical subspecialties. As a member of this team, the pharmacist plays a critical role in the provision of sterile admixtures, compatible nutritional formulations, and cost-effective, therapeutically equivalent strategies. The pharmacist has become more involved in the clinical care of the patient, with particular emphasis on the development of drug-induced metabolic disorders. The multitude of drugs prescribed to hospitalized patients increases the potential for serious metabolic disturbances. This is especially true in the critical care setting where sudden changes in metabolism (e.g., acid-base homeostasis, fluid and electrolyte balance) may result in profoundly negative effects. The critical care setting also represents the most sensitive period of hospitalization where even subtle changes in metabolic homeostasis may assume major clinical significance. Early recognition of offending agents and the institution of appropriate intervention may avert serious iatrogenic diseases. The nutrition support team is in a unique position to address many such disorders through selective manipulation of the various components in the parenteral nutrient admixture. The ability of the pharmacist to recognize the development of drug-induced metabolic disorders lends further support for clinical pharmacy in nutrition support services.

Abstract <strong>Objectives:</strong>&nbsp;To assess the nature of drug-related problems (DRPs) associated with antibiotic use and to evaluate the level of significance of clinical pharmacist interventions in surgical care.&nbsp;<strong>Methods:</strong>&nbsp;This prospective interventional study was carried out for 1 year in the wards of General Surgery. The in-patients were followed daily and reviewed for DRPs. The identified DRPs were categorized according to the Hepler and Strand classification. Drug-related problems other than those in Hepler and Strand classification were categorized separately based on medical literature and clinical practice evidence. The identified DRPs were conveyed and discussed by the clinical pharmacist with the surgeon, and ideal suggestions for further action. A bivariate logistic regression analysis was performed to identify the predictors of the DRPs associated with antibiotic use.&nbsp;<strong>Findings:</strong>&nbsp;Of the 614 patients, 64.49% were males. 414 DRPs were identified, wherein improper duration of drug use (20.86%) was the most common DRP. The significance level of pharmacist intervention was found to be &lsquo;major&rsquo; in 49% of cases. The incidence of DRPs associated with antibiotic use was 67.42%. The rate of acceptance of pharmacist interventions was 99%. A strong association between the occurrence of DRPs and age, the presence of two co-morbid conditions, the intake of two and three antibiotics, and length of stay (LOS) of 6&ndash;10 and 16&ndash;20 days (P &lt; 0.05) was noted.&nbsp;<strong>Conclusion:</strong>&nbsp;Even though antibiotics are necessary for surgical management, their vigilant use is warranted. The clinical pharmacist plays an enormous role in optimizing antibiotic therapy in surgical patient care.<strong>&nbsp;Novelty:</strong>&nbsp;To the best of our knowledge, such an interventional study of its kind that exclusively focused on the surgery ward was not conducted in India previously, and not many studies of a similar kind have been carried out in other parts of the world that have assessed the prevalence and frequency of DRPs related to antibiotic use, the predictors associated with the DRPs, the role of clinical pharmacists in the surgery ward, and the acceptance rate of pharmacist interventions by the surgeons. Here, an attempt to identify the predictors of drug-related problems associated with antibiotic use was made to help prevent and resolve drug-related problems earlier, therebypromising optimized therapy. Also, it highlights the importance of a clinical pharmacist&rsquo;s role in surgical care regarding patient safety. <strong>Keywords:</strong> Antibiotics; Surgery; Clinical Pharmacist; Predictors; Interventions; Drug Related Problems

CTC is a diagnostic method that has been developed for more than a decade and there is a large number of studies conducted to describe its capabilities. By using new generations of CT devices and advanced software, colon analysis is possible in a relatively short time. On the other hand, high sensitivity for detecting polyps and the possibility of detecting bowel abnormalities make CTC an interesting and desirable method. The aim of this article is to determine the advantages and disadvantages of CTC, as well as its role in colon cancer screening. Compared to colonoscopy, CTC is a less invasive technique that does not require sedation. In addition to its advantages, CTC is associated with several disadvantages. A review of research proved a very small percentage of complications that can occur during the procedure, and the main limitation is ionising radiation. Despite this, it is used for numerous indications and plays a role in detection of colorectal cancer. Due to the fact that colon cancer is a major problem in the world, its frequency and mortality are trying to be reduced by screening methods. Compared to other diagnostic methods, CTC is described as a pleasant and safe examination. Considering the available data, CTC represents an ideal balance due to minimal invasiveness and high sensitivity. With the advancement of technology, CT devices and software, the role of CTC will most probably grow significantly and secure its important place in healthcare.

Central nervous system (CNS) infections are a major source of morbidity and mortality despite the remarkable progress in prevention and treatment of infectious disease. Because of the difficulty of direct tissue sampling, imaging plays a crucial role in detecting, diagnosing, and monitoring the therapeutic response of CNS infections. An accurate diagnosis in CNS infections is especially rewarding, given the availability of potential effective antimicrobials.Bacterial, viral, fungal, and parasitic infections may affect the brain and/or its meningeal coverings, and can be characterized by a variety of radiological patterns that help narrow the differential diagnoses and eventually tailor the optimal management.This review addresses the typical imaging findings of intracranial infectious diseases in both immunocompetent and immunocompromised patients.

Spontaneous echo contrast (SEC) indicates blood stasis in cardiac chambers and major vessels, and is a known precursor of thrombus formation. Transesophageal echocardiography plays a pivotal role in detecting and grading SEC in the left atrial (LA) cavity. Assessing LA SEC can identify patients at increased risk for thromboembolic events. LA SEC also develops in patients who have sinus rhythm, especially in those with heart failure. Detection of LA SEC is not uncommon in subjects who have multiple cardiovascular comorbidities, although mechanisms behind this association are not fully understood. In patients with atrial fibrillation, the role of mitral regurgitation in counteracting LA SEC and subsequent thromboembolism is controversial. Moreover, alterations of blood coagulability and elevated levels of certain biological markers in the blood contribute to occurrence of LA SEC. This review describes the pathogenesis and assessment of SEC, in addition to the relationship between LA SEC and clinical, biological and echocardiographic parameters.

AbstractBreast Cancer is one of the most frequently diagnosed cancer diseases worldwide, and the most common invasive tumour for women. As with all cancers, early detection plays a major role in reducing the mortality and morbidity rate. Currently, most breast cancers are detected due to clinical symptoms, or by screening mammography. The limitations of these techniques have resulted in research of alternative methods for imaging and detecting breast cancer. Apart from this, it is essential to define precise tumour margins during breast-conserving surgeries to reduce the re-excision rate. This study presents the advances in the development of a silicon-based THz sub-wavelength imager usable in life science applications, especially for tumour margin identification.

Objective: The objective of this study was to study the prevalence and severity of possible drug-drug interactions (DDIs) among the geriatric patients.Methods: The present study was a retrospective cross-sectional study. Case records of geriatric inpatients from the medical records department were included in the study and the case records of all the remaining age group inpatients were excluded from the study. All the collected cases were subjected to check for the DDIs using the software Micromedex 2.0 and were categorized into minor, moderate, and major based on the severity.Results: In this study, a total of 85 cases were screened for possible DDIs, and among them, 54 cases were found to be with 179 possible DDIs. The prevalence was observed to be 63.5%. Most of the possible DDIs were found to be with moderate severity (65.4%) followed by major (25.7%). Majority of the possible DDIs were observed in the Department of General Medicine (83.2%) followed by chest and tuberculosis (7.8%).Conclusion: Majorly, the severity of interactions was found to be moderate in this study. To reduce the DDIs, rationale prescriptions have to be prescribed by considering the risk-benefit ratio. Geriatrics should be prescribed very cautiously because the age-related pharmacokinetics plays a significant role. By taking all the above aspects into consideration, clinical pharmacist should play a crucial role in the prevention and management of DDIs, especially in geriatrics.

In medical imaging, Computer Aided Diagnosis (CAD) has become one of the major research topics but is still in the infancy stage due to the lack of its full potential for applications to analyze the lesions obtained from various modalities. Pattern recognition and computer vision plays a significant role in clinical procedures for detecting and diagnosing different human diseases through the processing and analyzing of images acquired through various medical imaging modalities. In many cases of medical applications having high dimensional data characterized by huge number of features require large amount of memory and computation power. In order to tackle this problem, the aim is to construct a combination of feature that builds a unique model to provide better classification performance and accuracy. In this paper, we have conducted a survey on widely used approaches for feature selection and analyzed the purpose to investigate the strength and weakness of existing methods used in different types of modalities of images. Most of the work discussed in this literature review faces many limitations such as accuracy, cost, time and storage when dealing with huge amount of data. Our prime intention is to tackle these problems by building a uniform modal for feature selection to rank the features which are extracted from different medical image modalities to detect and diagnose the abnormalities present in those images in a most efficient way.

In medical imaging, Computer Aided Diagnosis (CAD) has become one of the major research topics but is still in the infancy stage due to the lack of its full potential for applications to analyze the lesions obtained from various modalities. Pattern recognition and computer vision plays a significant role in clinical procedures for detecting and diagnosing different human diseases through the processing and analyzing of images acquired through various medical imaging modalities. In many cases of medical applications having high dimensional data characterized by huge number of features require large amount of memory and computation power. In order to tackle this problem, the aim is to construct a combination of feature that builds a unique model to provide better classification performance and accuracy. In this paper, we have conducted a survey on widely used approaches for feature selection and analyzed the purpose to investigate the strength and weakness of existing methods used in different types of modalities of images. Most of the work discussed in this literature review faces many limitations such as accuracy, cost, time and storage when dealing with huge amount of data. Our prime intention is to tackle these problems by building a uniform modal for feature selection to rank the features which are extracted from different medical image modalities to detect and diagnose the abnormalities present in those images in a most efficient way.

The healthcare sector caters to millions of people and makes a significant contribution to the local economy. The inclusion of artificial intelligence and machine learning in healthcare is not only benefiting society but also overcoming various challenges associated with it. Artificial intelligence is a branch of computer science that is used to induce human-like intelligence into machines. Machine learning is a subset of artificial intelligence that makes machines capable of learning and giving the desired conclusions without explicit programming and human support. Machine learning in the healthcare sector is making huge advancements and yielding positive results. The increasing applications of machine learning have earned it a valuable spot in the healthcare sector. From specialized robots in hospitals to automated software for disease prediction and detection, machine learning is taking over almost all areas of healthcare with the aim of reducing the workload of medical experts and also delivering services to individuals at home with cost-effective solutions. With the advancement of technology, the introduction of portable systems has led to the availability of enormous amounts of medical data, which is difficult to analyze by human experts because it takes a lot of time, effort, and analytical costs. Machines are better in speed, endurance, and pattern identification as compared to humans. With the introduction of machine learning in healthcare, the task of managing massive data has become easier as automated machine learning models not only help in data analysis but are also capable of detecting underlying data patterns that may be difficult for clinical experts to come across. Machine learning can ease the task of identifying and detecting various diseases by providing complex algorithms such as Artificial Neural Networks (ANNs). With the introduction of neural networks, the analysis can be done on various data parameters given their ability to self-learn, memorize, and provide quality treatment. Machine learning not just focuses on the physical well-being of an individual but also their mental health by coming up with artificial-intelligence-based mood trackers and self-assessing applications for stress diagnosis. One of the major applications of machine learning is to detect and identify dangerous diseases, such as diabetes and cancer, that are difficult to detect at the initial stage and are detected at subsequent stages when it is too late. The use of early detection systems can save many lives by providing timely treatment of patients. Another important application of machine learning in the healthcare field is the introduction of bionic microchips. The fusion of bionics and machine learning will bring a revolutionary change in the healthcare sector. One such example is implanting bionic chips in the brain to monitor brain activity for the identification of neurological disorders like epilepsy. The AIenabled bionic hand uses a man-machine interface to interpret the patient's intent and send the commands to the artificial limb, thus helping the patient make more natural movements and controlling the prosthetics more precisely. There is a tremendous use of machine learning and artificial intelligence in providing customized solutions to patients, as one solution does not cater to many patients. Therefore, customized solutions according to their medical history are a feasible choice. Machine learning plays an enormous role in drug discovery by improving decision-making in pharmaceutical data through high-quality data. It provides immediate assistance to the patients using the healthcare chatbot systems that suggest immediate solutions to them. There is no area left in the healthcare industry of which machine learning is not a part. Machine learning in the healthcare industry can yield efficient and timely results without any human intelligence. This is just the beginning. Machine learning in healthcare has a bright future that will revolutionize the field of medicine and healthcare.

Abstract Significant research involving the isolation and detection of circulating tumor cells (CTCs) has become prevalent in the field of biomedicine. It plays a crucial role in the diagnosis and treatment of cancer and has made substantial strides in recent years. A major event in the timeline of cancer is metastasis, a set of occurrences where cells are shed from a cancerous site, then flow through the circulatory system and seed themselves throughout the body, forming secondary tumors. There are few observable symptoms in the early stages of metastasis and this fact severely limits clinical treatment. The fabrication and preliminary testing of a magnetophoretic bioseparation chip capable of isolating and detecting CTCs from peripheral blood, which can aid in early detection of metastases, is presented in this work. MCF7 breast cancer cells along with superparamagnetic microparticles, which are specifically coated with anti-EpCAM to bind to the cancer cells, are spiked into a blood sample. After the spiked blood sample is introduced into the biochip, a locally engineered magnetic field gradient captures the magnetically labeled cancer cells while the non-target cells are allowed to pass by. Once the target cells are isolated from the blood sample, flow cytometry is used to determine the recovery rate of the magnetophoretic device. The proposed device can operate at continuous flow rates magnitudes higher than other CTC isolation devices and is fabricated using much simpler methods which make it quite unique. These properties combined with greater than 80% recovery rates make the device quite favorable for economic point of care use in clinical applications.