Anxiolytic and Antidepressant Effect of Phaseolus vulgaris on Animal Models.

An experimental study was conducted using rodents at different doses to evaluate the effect of Phaseolus vulgaris (red beans) on cage crossing, head dip, open field, elevated plus maze, and light and dark apparatus for anxiety and forced swim test for depression. The corticosterone level and histopathological evaluation was also done to correlate the antidepressive impact of the red beans. The study also identified the components responsible for the effect using GCMS. Based on the findings, red beans could be a potential non-pharmacological therapy for mild to moderate depressive patients. The anxiety model was conducted on mice weighing 20-25 gms. Group I was taken as control, group II as 500 mg/kg and group III as administered 1000 mg/kg. The tests were performed on 0th, 7th, 15th, 30th, 45th, and 60th day. The depression model research was conducted on albino rats weighing between 180 and 200 g, divided into four groups: a control group, a 500 mg/kg Phaseolus vulgaris group, a 1000 mg/kg Phaseolus vulgaris group, and a standard group treated with fluoxetine. The forced swimming test was performed on days 0, 7, 15, 30, 45, and 60, after which histopathological evaluations were conducted and blood samples were taken to assess corticosterone levels. GCMS was used to identify the constituents present in red beans, while optical spectroscopy was used to detect minerals and ions. Results showed that both doses of Phaseolus vulgaris possess anxiolytic effect and increased the struggling time of rats in depression model significantly, with the 1000 mg/kg dose showing more significant results than the 500 mg/kg dose. The GCMS results identified the presence of erucic acid, which causes an increase in α-amylase, thus reducing depression. Optical spectroscopy also showed that red beans contain zinc, which may increase BDNF and help in treating depression.

Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors - Analytical Validation.

Background: Neuroendocrine tumors (NETs) represent a diverse group of neoplasms that arise from neuroendocrine cells, with Ki-67 immunostaining serving as a crucial biomarker for assessing tumor proliferation and prognosis. Accurate and reliable quantification of Ki-67 labeling index is essential for effective clinical management. Methods: We aimed to evaluate the performance of open-source/open-access deep learning cloud-native platform, DeepLIIF (https://deepliif.org), for the quantification of Ki-67 expression in gastrointestinal neuroendocrine tumors and compare it with the manual quantification method. Results: Our results demonstrate that the DeepLIIF quantification of Ki-67 in NETs achieves a high degree of accuracy with an intraclass correlation coefficient (ICC) = 0.885 with 95% CI (0.848-0.916) which indicates good reliability when compared to manual assessments by experienced pathologists. DeepLIIF exhibits excellent intra- and inter-observer agreement and ensures consistency in Ki-67 scoring. Additionally, DeepLIIF significantly reduces analysis time, making it a valuable tool for high-throughput clinical settings. Conclusion: This study showcases the potential of open-source/open-access user-friendly deep learning platforms, such as DeepLIIF, for the quantification of Ki-67 in neuroendocrine tumors. The analytical validation presented here establishes the reliability and robustness of this innovative method, paving the way for its integration into routine clinical practice. Accurate and efficient Ki-67 assessment is paramount for risk stratification and treatment decisions in NETs and AI offers a promising solution for enhancing diagnostic accuracy and patient care in the field of neuroendocrine oncology.

Use of a Novel Deep Learning Open-Source Model for Quantification of Ki-67 in Breast Cancer Patients in Pakistan: A Comparative Study between the Manual and Automated Methods.

Introduction: Breast cancer is the most common cancer in women; its early detection plays a crucial role in improving patient outcomes. Ki-67 is a biomarker commonly used for evaluating the proliferation of cancer cells in breast cancer patients. The quantification of Ki-67 has traditionally been performed by pathologists through a manual examination of tissue samples, which can be time-consuming and subject to inter- and intra-observer variability. In this study, we used a novel deep learning model to quantify Ki-67 in breast cancer in digital images prepared by a microscope-attached camera. Objective: To compare the automated detection of Ki-67 with the manual eyeball/hotspot method. Place and duration of study: This descriptive, cross-sectional study was conducted at the Jinnah Sindh Medical University. Glass slides of diagnosed cases of breast cancer were obtained from the Aga Khan University Hospital after receiving ethical approval. The duration of the study was one month. Methodology: We prepared 140 digital images stained with the Ki-67 antibody using a microscope-attached camera at 10×. An expert pathologist (P1) evaluated the Ki-67 index of the hotspot fields using the eyeball method. The images were uploaded to the DeepLiif software to detect the exact percentage of Ki-67 positive cells. SPSS version 24 was used for data analysis. Diagnostic accuracy was also calculated by other pathologists (P2, P3) and by AI using a Ki-67 cut-off score of 20 and taking P1 as the gold standard. Results: The manual and automated scoring methods showed a strong positive correlation as the kappa coefficient was significant. The p value was <0.001. The highest diagnostic accuracy, i.e., 95%, taking P1 as gold standard, was found for AI, compared to pathologists P2 and P3. Conclusions: Use of quantification-based deep learning models can make the work of pathologists easier and more reproducible. Our study is one of the earliest studies in this field. More studies with larger sample sizes are needed in future to develop a cohort.

Current Practice Of Histopathology In Pakistan: Difficulties, Challenges, And Solutions.

Histopathology is the gold standard for diagnosis of cancers as well as many non 14 neoplastic diseases. Pakistan is a country of more than 220 million people and the fifth most populated country of the world. Unfortunately, it has a weak healthcare system in general and poor pathology services in particular. Till date, only 338 histopathologists have passed their fellowship examination in Pakistan; this has led to a very alarming situation considering the marked increase in the prevalence of cancer cases and other diseases which need histopathological interpretation. There are only 18 big histopathological labs in the country, the majority of which are located in major cities which further delays the diagnosis of patients who live in rural areas. Immediate steps are required for better histopathology services in the country. Adoption of digital tools may bridge the gaps of histopathology-practice and ensure consistency across the country.

Ki-67 Quantification in Breast Cancer by Digital Imaging AI Software and its Concordance with Manual Method.

OBJECTIVE: To validate the concordance of automated detection of Ki67 in digital images of breast cancer with the manual eyeball / hotspot method. STUDY DESIGN: Descriptive study. Place and Duration of the Study: Jinnah Sindh Medical University, Karachi, from 1st January to 15th February 2022. METHODOLOGY: Glass slides of cases diagnosed as invasive ductal carcinoma (IDC) were obtained from the Agha Khan Medical University Hospital, selected retrospectively and randomly from 60 patients. They were stained with the Ki67 antibody. An expert pathologist evaluated the Ki67 index in the hotspot fields using eyeball method. Digital images were taken from the hotspots using a camera attached to the microscope. The images were uploaded in the Mindpeak software to detect the exact percentage of Ki67-positive cells. The results obtained through automated detection were compared with the results reported by expert pathologists to see the differential outcome. RESULTS: The manual and automated scoring methods showed strong positive concordance (p <0.001). CONCLUSION: Automated scoring of Ki-67 staining has tremendous potential as the issues of lack of consistency, reproducibility, and accuracy can be eliminated. In the era of personalised medicine, pathologists can efficiently give a precise clinical diagnosis with the support of AI. KEY WORDS: Artificial intelligence, Algorithms, Breast cancer, Deep learning, Image detection, Ki-67.

A suggested way forward for adoption of AI-Enabled digital pathology in low resource organizations in the developing world.

Low- and middle-income countries (LMICs) represent a big source of data not only for endemic diseases but also for neoplasms. Data is the fuel which drives the modern era. Data when stored in digital form can be used for constructing disease models, analyzing disease trends and predicting disease outcomes in various demographic regions of the world. Most labs in developing countries don't have resources such as whole slide scanners or digital microscopes. Owing to severe financial constraints and lack of resources, they don't have the capability to handle large amounts of data. Due to these issues, precious data cannot be saved and utilized properly. However, digital techniques can be adopted even in low resource settings with significant financial constraints. In this review article, we suggest some of the options available to pathologists in developing countries which can enable them to start their digital journey and move forward despite resource-poor health system.

A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology.

Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, It is aggressive and has poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three standard criteria (i.e., mitosis count, necrosis, and nuclear atypia). Among these, mitosis count is the most important and challenging biomarker. In general, pathologists use the traditional manual counting method for the detection and counting of mitosis. This procedure is very time-consuming, tedious, and subjective. To overcome these challenges, artificial intelligence (AI) based methods have been developed that automatically detect mitosis. In this paper, we propose a new ULMS dataset and an AI-based approach for mitosis detection. We collected our dataset from a local medical facility in collaboration with highly trained pathologists. Preprocessing and annotations are performed using standard procedures, and a deep learning-based method is applied to provide baseline accuracies. The experimental results showed 0.7462 precision, 0.8981 recall, and 0.8151 F1-score. For research and development, the code and dataset have been made publicly available.

Adoption of Digital Pathology in Developing Countries: From Benefits to Challenges.

Digital pathology and the use of artificial intelligence constitute undisputedly the future of modern pathology. The outcomes and benefits of the whole slide imaging are beyond the scope of traditional microscopy, which the pathologists were using for decades. COVID-19 pandemic has further highlighted the importance of digital pathology as it offers the pathologists to work from their place of comfort and bridges the gap of physical barriers. In addition to the many advantages, there are certain limitations and challenges, which have to be overcomed particularly in the developing world. The major issue is the cost of scanners and technical support and training of staff. However, despite all these problems and challenges that exist, these can be resolved with the passage of time, where the role of world leader organisations will be of great importance in resolving these challenges. Key Words: Digital pathology, Artificial intelligence, Whole slide imaging.

Knowledge of obstructive sleep apnoea in final year medical students and junior doctors-a multi-centre cross-sectional study.

BACKGROUND: Sleep medicine is an under-recognised medical specialty in Pakistan and obstructive sleep apnoea (OSA) often goes unnoticed. Final year medical students and junior doctors are the primary medical contact to elicit patient history and physical examination. We aimed to measure the current knowledge of OSA amongst the final year medical students and junior doctors at four university teaching hospitals across three large Pakistani cities. METHODS: Cross-sectional survey of final year medical students and junior doctors rotating through medical wards of four university teaching hospitals were conducted during August-October 2019. The knowledge section of the OSA knowledge and attitude (OSAKA) questionnaire was used. Descriptive statistics were used to present the data with Chi-Square test and independent samples student t-test to compare the differences between individual items and mean scores of the participants, respectively. RESULTS: A total 282 final year medical students and 204 junior doctors completed the survey yielding a response rate of 53% for medical students and 97% for junior doctors. The knowledge of sleep apnoea was poor in both groups of participants with a mean score of 7.6 (42%) on the knowledge scale of OSAKA questionnaire. Medical students scored higher on the item related to snoring as the most prevalent symptom in OSA patients when compared to the junior doctors (χ2  = 8.92, P = 0.003). More junior doctors responded correctly about the role of uvulopalatopharyngoplasty in the management of OSA when compared to the medical students (χ2  = 5.14, P = 0.02). Differences in scores of both groups of participants on other items were small and did not reach statistical significance. CONCLUSION: Final year medical students and junior doctors from a sample of four university teaching hospitals in three large cities of Pakistan have limited knowledge about the diagnosis and management of OSA. The observed limited knowledge of OSA may contribute towards under-diagnosis of this increasingly prevalent medical condition.

Morphological study of Pipelle biopsy specimens in cases of abnormal uterine bleeding.

OBJECTIVE: To determine the frequencies of common morphological patterns of abnormal uterine bleeding on Pipelle biopsy specimen. METHODS: The cross-sectional study was conducted at PNS Shifa Hospital, Karachi, and comprised endometrial Pipelle biopsies of patients with abnormal uterine bleeding received between January 2013 and January 2014. Patient's age, marital status, parity and histopatholgical spectrum were recorded. SPSS 17 was used for data analysis. RESULTS: Of the 101 patients, 53(52.50%) presented with proliferative endometrium, 22(21.80%) had secretory endometrium, 13(12.9%) presented with chronic non-specific endometritis, 8(7.9%) had endometrial hyperplasia without atypia, and 5(5%) had endometrial hyperplasia with atypia. Besides, 86(85.1 %) were nulliparous; 15(14.9%) were parous; 92(91.1%) were married and 9(8.9%) were unmarried. CONCLUSIONS: The most common morphological pattern was proliferative endometrium. Though Pipelle has its own limitations, it performed better when endometrial pathology was global rather than focal.