Abnormal liver enzymes: A review for clinicians

The objective of this study was to evaluate the use of social media among healthcare workers in an attempt to identify how it affects the quality of patient care. An anonymous survey of 35 questions was conducted in South Texas, on 366 healthcare workers. Of the 97% of people who reported owning electronic devices, 87.9% indicated that they used social media. These healthcare workers indicated that they spent approximately 1 h on social media every day. The healthcare workers below the age of 40 were more involved in social media compared to those above 40 (p < 0.05). The use of social media among physicians and nurses was noted to be identical (88% for each group), and both groups encouraged their patients to research their clinical conditions on social media (p < 0.05). A higher number of physicians reported awareness of a social media policy in their hospital compared to nurses (p < 0.05). However, a large proportion of healthcare workers (40%) were unaware of their workplace policy, which could potentially cause a privacy breach of confidential medical information. Further studies are required to evaluate specific effects of these findings on the quality of patient care.

Extended work hours, interrupted sleep, and shift work are integral parts of medical training among all specialties. The need for 24-hour patient care coverage and economic factors have resulted in prolonged work hours for resident physicians. This has traditionally been thought to enhance medical educational experience. These long and erratic work hours lead to acute and chronic sleep deprivation and poor sleep quality, resulting in numerous adverse consequences. Impairments may occur in several domains, including attention, cognition, motor skills, and mood. Resident performance, professionalism, safety, and well–being are affected by sleep deprivation, causing potentially adverse implications for patient care. Studies have shown adverse health consequences, motor vehicle accidents, increased alcohol and medication use, and serious medical errors to occur in association with both sleep deprivation and shift work. Resident work hour limitations have been mandated by the Accreditation Council for ...

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.

Diabetes mellitus (DM) is a highly prevalent condition affecting about 347 million people worldwide. In addition to its numerous clinical implications, DM also exerts a negative effect on patient's sleep quality. Impaired sleep quality disrupts the adequate glycemic control regarded as corner stone in DM management and also lead to many deleterious effects causing a profound impact on health related quality of life. This article outlines various factors leading to impaired sleep quality among diabetics and delineates how individual factor influences sleep. The article also discusses potential interventions and lifestyle changes to promote healthy sleep among diabetics.

Obstructive sleep apnea (OSA) is a well-known sleep ailment. OSA mostly occurs due to the shortage of oxygen for the human body, which causes several symptoms (i.e., low concentration, daytime sleepiness, and irritability). Discovering the existence of OSA at an early stage can save lives and reduce the cost of treatment. The computer-aided diagnosis (CAD) system can quickly detect OSA by examining the electrocardiogram (ECG) signals. Over-serving ECG using a visual procedure is challenging for physicians, time-consuming, expensive, and subjective. In general, automated detection of the ECG signal’s arrhythmia is a complex task due to the complexity of the data quantity and clinical content. Moreover, ECG signals are usually affected by noise (i.e., patient movement and disturbances generated by electric devices or infrastructure), which reduces the quality of the collected data. Machine learning (ML) and Deep Learning (DL) gain a higher interest in health care systems due to its ability of achieving an excellent performance compared to traditional classifiers. We propose a CAD system to diagnose apnea events based on ECG in an automated way in this work. The proposed system follows the following steps: (1) remove noise from the ECG signal using a Notch filter. (2) extract nine features from the ECG signal (3) use thirteen ML and four types of DL models for the diagnosis of sleep apnea. The experimental results show that our proposed approach offers a good performance of DL classifiers to detect OSA. The proposed model achieves an accuracy of 86.25% in the validation stage.