Recent progress and the emerging role of lncRNAs in cancer drug resistance; focusing on signaling pathways.

It is becoming more and more apparent that many of the genetic alterations associated with cancer are located in areas that do not encode proteins. lncRNAs are a class of RNAs that do not code for proteins but play a crucial role in maintaining cell function and regulating various cellular processes. By doing this, they have recently introduced what may be a brand-new and essential layer of biological control. These have more than 200 nucleotides and are linked to several diseases; as a result, they have become potential tools for therapeutic intervention. Emerging technologies suggest the presence of mutations on genomic loci that give rise to lncRNAs rather than proteins in a disease as complex as cancer. These lncRNAs play essential parts in gene regulation, which impacts several cellular homeostasis processes, including proliferation, survival, migration, and genomic stability. The leading cause of death in the world today is cancer. Delays in diagnosis and a lack of standard and efficient treatments are the leading causes of the high death rate. Clinically, surgery is frequently used successfully to remove cancers that have not spread, but it is less successful in treating metastatic cancer, which has a drastically lower chance of survival. Chemotherapeutic drugs are a typical therapy to treat the cancer that has spread to other organs. Drug resistance to chemotherapy, however, presents a significant challenge to achieving positive outcomes and is frequently the cause of treatment failure. A substantial barrier to progress in medical oncology is cancer drug resistance. Resistance can develop clinically either before or after cancer treatment. According to this study, lncRNAs influence drug resistance through several different methods. LncRNAs often impact drug resistance by controlling the expression of a few intermediary regulatory variables rather than by directly affecting drug resistance. Additionally, lncRNAs have a variety of roles in cancer medication resistance. Most lncRNAs induce drug resistance when overexpressed; however, other lncRNAs have inhibitory effects. This study provides an overview of the current understanding of lncRNAs, relevance to cancer, and potential therapeutic applications.

Space-time cluster analysis of anemia in pregnant women in the province of Khyber Pakhtunkhwa, Pakistan (2014-2020).

Anaemia is a common public-health problem affecting about two-thirds of pregnant women in developing countries. Spacetime cluster analysis of anemia cases is important for publichealth policymakers to design evidence-based intervention strategies. This study discovered the potential space-time clusters of anemia in pregnant women in Khyber Pakhtunkhwa Province, Pakistan, from 2014 to 2020 using space-time scan statistic (SatScan). The results show that the most likely cluster of anemia was seen in the rural areas in the eastern part of the province covering five districts from 2017 to 2019. However, three secondary clusters in the West and one in the North were still active, signifying important targets of interest for public-health interventions. The potential anemia clusters in the province's rural areas might be associated with the lack of nutritional education in women and lack of access to sufficient diet due to financial constraints.

Improved UNet Deep Learning Model for Automatic Detection of Lung Cancer Nodules.

Uncontrolled cell growth in the two spongy lung organs in the chest is the most prevalent kind of cancer. When cells from the lungs spread to other tissues and organs, this is referred to as metastasis. This work uses image processing, deep learning, and metaheuristics to identify cancer in its early stages. At this point, a new convolutional neural network is constructed. The predator technique has the potential to increase network architecture and accuracy. Deep learning identified lung cancer spinal metastases in as energy consumption increased CT readings for lung cancer bone metastases decreased. Qualified physicians, on the other hand, discovered 71.14 and 74.60 percent of targets with energies of 140 and 60 keV, respectively, whereas the proposed model gives 76.51 and 81.58 percent, respectively. Expert physicians' detection rate was 74.60 percent lower than deep learning's detection rate of 81.58 percent. The proposed method has the highest accuracy, sensitivity, and specificity (93.4, 98.4, and 97.1 percent, respectively), as well as the lowest error rate (1.6 percent). Finally, in lung segmentation, the proposed model outperforms the CNN model. High-intensity energy-spectral CT images are more difficult to segment than low-intensity energy-spectral CT images.

Impact of Medical TV Shows on the Surgical Knowledge of Non-Healthcare Students of Lahore, Pakistan.

Purpose: A popular genre of television shows is medical dramas. Although the primary objective of watching these shows is entertainment, acquiring medical knowledge is a passive by-product. Surgical procedures constitute a large part of the storyline of these shows. This could either serve as a source of medical knowledge or provide false information, the effect being especially important in individuals with no prior medical exposure. This study assesses the impact medical TV shows can have on the surgical knowledge of non-healthcare students and the difference in knowledge between different demographic groups (among those with relatives in the medical community and those without). Methods: A cross-sectional study was conducted among the non-healthcare students of Lahore, Pakistan. A self-administered questionnaire was used containing socio-demographic factors (age, gender, educational discipline), history, and hours of medical TV shows watched. It also contained ten questions each with a score of 1 to assess surgical knowledge. Data were analyzed using SPSS v.26. Results: Among the 1097 respondents, 450 (41%) had a history of watching medical TV shows. The majority, 319 (29.1%), had seen these shows for < 24 hours. The mean score of all respondents was 5.79 out of a maximum score of 10. Respondents with a history of watching medical TV shows were more knowledgeable than those who did not (p < 0.001). Similarly, respondents with a history of watching more hours of medical TV shows were more knowledgeable than those who watched for a lesser number of hours (p < 0.001). Respondents with relatives in the healthcare profession were also more knowledgeable than those without (p = 0.049). Conclusion: If properly developed, while maintaining their primary entertainment value, medical TV shows can also be used as efficient learning tools. Quality controls must also be applied to minimize the risk of false information.

Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image.

Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person's chances of survival, an early diagnosis is a necessary prerequisite. In this regard, the existing methods of tumour detection, such as CT scans, are most commonly used to recognize infected regions. Despite this, there are certain obstacles presented by CT imaging, so this paper proposes a novel model which is a correlation-based model designed for analysis of lung cancer. When registering pictures of thoracic and abdominal organs with slider motion, the total variation regularization term may correct the border discontinuous displacement field, but it cannot maintain the local characteristics of the image and loses the registration accuracy. The thin-plate spline energy operator and the total variation operator are spatially weighted via the spatial position weight of the pixel points to construct an adaptive thin-plate spline total variation regular term for lung image CT single-mode registration and CT/PET dual-mode registration. The regular term is then combined with the CRMI similarity measure and the L-BFGS optimization approach to create a nonrigid registration procedure. The proposed method assures the smoothness of interior of the picture while ensuring the discontinuous motion of the border and has greater registration accuracy, according to the experimental findings on the DIR-Lab 4D-CT public dataset and the CT/PET clinical dataset.

A Quantitative Study of Empathy in Pakistani Medical Students: A Multicentered Approach.

OBJECTIVE: To examine the empathy level of undergraduate medical students in Pakistan. Three hypotheses are developed based on the literature review. (1) Female medical students have a higher level of empathy than do male students. (2) Empathy scores vary during the medical school years in Pakistani students. (3) Medical students interested in people-oriented specialties would score higher than the students interested in technology-oriented specialties. METHODS: This is a quantitative inquiry approach using a cross-sectional design of 1453 students from 8 Pakistani medical schools, both private and state. The sample consists of 41.1% (n = 597) male students and 58.9% (n = 856) female students. Empirical data are collected using the Jefferson Scale of Physician Empathy (JSPE), a well-validated self-administered questionnaire. RESULTS: The mean empathy score among students is 4.77 with a standard deviation of 0.72. The results show that there is no statistically significant association between the empathy scores and gender, t(1342.36) = -0.053, P = .95). There is a statistically significant difference between the empathy scores and the years of medical school, F(14, 1448) = 4.95, P = .01. Concerning the specialty interests, there is no statistically significant difference between the empathy score and specialty interests. CONCLUSION: The findings of this study showed that in Western countries, medical students performed better than Pakistani medical students on the empathy scale. This finding has important implications for Pakistani medical educators to improve the interpersonal skills of medical students in the context of patient care. Inconsistent with our expectations and experiences, our findings do not support that female medical students scored better than their male counterparts on the empathy scale. Because of the nature of a cross-sectional study, it is impossible to argue the decline of empathy during medical school training.