Shark fish oil prevents scopolamine-induced memory impairment in an experimental model.

Fish oil has been known for its antioxidant, cardioprotective, anti-inflammatory, and neuroprotective characteristics due to the presence of polyunsaturated fatty acids (PUFAs) that are essential for optimal brain function and mental health. The present study investigated the effect of Carcharhinus Bleekeri (Shark Fish) oil on learning and memory functions in scopolamine-induced amnesia in rats. Locomotor and memory-enhancing activity in scopolamine-induced amnesic rats was investigated by assessing the open field and passive avoidance paradigm. Forty male Albino mice were divided into 4 equal groups (n = 10) as bellow: 1 - control (received 0.9% saline), 2 - SCOP (received scopolamine 2 mg/kg for 21 days), 3 - SCOP + SFO (received scopolamine and fish oil 5 mg/kg/ day for 21 days), 4 - SCOP + Donepezil groups (received 3 mg/kg/day for 21 days). SFO produced significant (P < 0.01) locomotor and memory-enhancing activities in open-field and passive avoidance paradigm models. Additionally, SFO restored the Acetylcholine (ACh) concentration in the hippocampus (p < 0.05) and remarkably prevented the degradation of monoamines. Histology of brain tissue showed marked cellular distortion in the scopolamine-treated group, while the SFO treatment restored distortion in the brain's hippocampus region. These results suggest that the SFO significantly ameliorates scopolamine-induced spatial memory impairment by attenuating the ACh and monoamine concentrations in the rat's hippocampus.

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.

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.

Practice and effect of pesticide use on farmers' health in Sindh, Pakistan.

OBJECTIVE: To identify the frequency and mode of pesticide application, its adverse health effects, and to assess farmers' knowledge and practice of precautionary measures. METHODS: The cross-sectional study was conducted in Matli, Badin in the Sindh province of Pakistan from February to April 2020, and comprised farmers. Data was collected through face-to-face interview using a validated questionnaire. Data was analysed using SPSS 22. RESULTS: Of the 332 farmers, 329(99.1%) were males. The overall mean age was 31.18±12.79 years (range: 21-30 years). Also, 269(81%) of them had not attended formal schooling, and the mean duration of farming practice was 1.64±0.859 years.` Further, 136(41%) participants did not follow the recommended frequency of pesticide application, accidental ingestion was reported by 208(62.7%) participants, and inhalation by 203(61.1%). Dumping of pesticide containers in open fields was a common practice for 247(74.4%) subjects, and 223(67.2%) showed interest in safety training sessions. CONCLUSIONS: Farmers of Sindh were found to be seriously exposed to pesticide risk because of inadequate knowledge.

Evaluation of Acute and Chronic Antidiabetic Activity of Ivy (Hedera helix L.) Aqueous Leaf Extract in Rat Model.

BACKGROUND AND OBJECTIVE: Hedera helix L. (Ivy) has been utilized as an alternative medicine for cough however, through extensive literature search; we found no reported activity of ivy on α-glucosidase inhibition, HbA1c levels and its protective effect on vital organs. Therefore, the present study aimed to evaluate the antidiabetic and protective effect of ivy in alloxan induced rat model. MATERIALS AND METHODS: The hypoglycemic activity of ivy was examined in normoglycemic, glucose overloaded and alloxan-induced rats. The antidiabetic potential was also confirmed by estimation of HbA1c and α-glucosidase inhibitory activity. RESULTS: Results of acute and chronic study revealed that ivy produced highly significant decline (p<0.01) in fasting and post-prandial blood sugar levels as compared to diabetic control and standard group respectively. Furthermore, highly significant decline (p<0.01) in HbA1c levels were seen after chronic administration of ivy indicating its therapeutic effect in lowering HbA1c levels during long term use. It was found that ivy produced stronger and highly significant (p<0.05) inhibition of α-glucosidase activity than the standard agent acarbose at 500 µg mL-1. CONCLUSION: The histopathological studies of vital organs revealed protective effect of ivy via maintaining the normal architecture as compared to alloxan model. Hence, our findings support the potential use of ivy for diabetes management.