Mediating Role of Death Anxiety between Supernatural Beliefs and Life Satisfaction among Muslim Adults

The belief in supernatural forces is so pervasive in Pakistani society that it is nearly universal among illiterate and semi-literate people. Few literate individuals also subscribe to the idea of supernatural beings and entities. Such beliefs may invoke anxieties resulting in reduced satisfaction with life. In the current study, a cross-sectional survey of Muslim people was used to examine the potential mediation effect of death anxiety between supernatural beliefs and life satisfaction. A purposive sample (N = 220; with equal representation of both genders) of adults was recruited from different areas of district Sargodha. The supernatural Belief Scale, Arabic Scale of Death Anxiety, and Satisfaction with Life Scale were employed for assessing supernatural belief, death anxiety, and life satisfaction, respectively. Path analysis in Amos revealed the positive direct effect of supernatural belief on death anxiety and the negative direct effect of death anxiety on life satisfaction. Supernatural beliefs also demonstrated a negative indirect effect on life satisfaction through death anxiety. Overall, we found that individuals with supernatural beliefs were more likely to feel death anxiety, which might make them less satisfied with their life. Clinicians and mental health practitioners must envisage such therapeutic strategies as could counter the supernatural beliefs of the clients so that they might enjoy a more fulfilling and satisfying life

Association of Serum PSA Levels with Histopathological Pattern of Prostate Lesions

Background: Pathological changes that mainly affect prostate gland are prostatitis, benign prostatic hyperplasia (BPH) and cancerous lesions. Digital rectal examination (DRE), Transrectal Ultrasonography (TUS), and prostate specific antigen (PSA) followed by histopathological examination, are routinely used tests for diagnosis of prostate lesions. The aim of the present study is to determine the role of serum PSA levels in differentially diagnosing the different types of prostate lesions.Material and Methods: This retrospective (observational) study was conducted in Ibn-e-Sina Hospital Multan. Data of 2189 patients who were operated from 2007 to 2017 due to prostatic lesions were included in this analysis. Patients with BPH, prostatitis, prostate carcinoma and Prostatic Intraepithelial Neoplasia (PIN) were grouped according to serum PSA levels (ranging from 0 to >100 ng/ml) into five groups. Frequencies and percentages were calculated for different histopathological findings. Association of PSA levels with different histological patterns was determined with chi-square test with P-value < 0.05 taken as significant difference.Results: Mean age of patients was 62.45+10.64 years. On histopathology, BPH was diagnosed in 1676 (76.56%) patients, prostatitis in 133 (6.07%), carcinoma in 378 (17.26%) and PIN in 02 (0.09%) patients, respectively. Serum PSA levels of 4.01-10 ng/ml were found in 1050 (62.64%) BPH patients and in 59 (44.36%) prostatitis patients. Serum PSA levels of 10.01-20 ng/ml were found in only 40 (2.4%) BPH patients, 47 (35.33%) prostatitis patients, 22 (5.82%) carcinoma patients and in 1 (50.0%) PIN patient. Serum PSA levels of 20.01-100 ng/ml were found in 32 (1.9%) BPH patients, 11 (8.27%) prostatitis patients, 302 (79.89%) carcinoma patients, and in 1 (50.0%) PIN patient. Serum PSA levels of >100 ng/ml were absent in patients with BPH and PIN, and present in 1 (0.75%) prostatitis and 54 (14.28%) carcinoma patients.Conclusion: Benign prostatic hyperplasia was the commonest lesion in our patients (76.56%) with serum PSA levels >10 ng/ml reported in all patients with prostate carcinoma and prostatic intraepithelial neoplasia (PIN) patients

Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images : a retrospective study

Background: Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pathways and mutations from whole-slide images of haematoxylin and eosin-stained colorectal cancer slides as an alternative to current tests. Methods: In this retrospective study, we used 502 diagnostic slides of primary colorectal tumours from 499 patients in The Cancer Genome Atlas colon and rectal cancer (TCGA-CRC-DX) cohort and developed a weakly supervised deep learning framework involving three separate convolutional neural network models. Whole-slide images were divided into equally sized tiles and model 1 (ResNet18) extracted tumour tiles from non-tumour tiles. These tumour tiles were inputted into model 2 (adapted ResNet34), trained by iterative draw and rank sampling to calculate a prediction score for each tile that represented the likelihood of a tile belonging to the molecular labels of high mutation density (vs low mutation density), microsatellite instability (vs microsatellite stability), chromosomal instability (vs genomic stability), CpG island methylator phenotype (CIMP)-high (vs CIMP-low), BRAFmut (vs BRAFWT), TP53mut (vs TP53WT), and KRASWT (vs KRASmut). These scores were used to identify the top-ranked titles from each slide, and model 3 (HoVer-Net) segmented and classified the different types of cell nuclei in these tiles. We calculated the area under the convex hull of the receiver operating characteristic curve (AUROC) as a model performance measure and compared our results with those of previously published methods. Findings: Our iterative draw and rank sampling method yielded mean AUROCs for the prediction of hypermutation (0·81 [SD 0·03] vs 0·71), microsatellite instability (0·86 [0·04] vs 0·74), chromosomal instability (0·83 [0·02] vs 0·73), BRAFmut (0·79 [0·01] vs 0·66), and TP53mut (0·73 [0·02] vs 0·64) in the TCGA-CRC-DX cohort that were higher than those from previously published methods, and an AUROC for KRASmut that was similar to previously reported methods (0·60 [SD 0·04] vs 0·60). Mean AUROC for predicting CIMP-high status was 0·79 (SD 0·05). We found high proportions of tumour-infiltrating lymphocytes and necrotic tumour cells to be associated with microsatellite instability, and high proportions of tumour-infiltrating lymphocytes and a low proportion of necrotic tumour cells to be associated with hypermutation. Interpretation: After large-scale validation, our proposed algorithm for predicting clinically important mutations and molecular pathways, such as microsatellite instability, in colorectal cancer could be used to stratify patients for targeted therapies with potentially lower costs and quicker turnaround times than sequencing-based or immunohistochemistry-based approaches. Funding: The UK Medical Research Council

Deep learning based digital cell profiles for risk stratification of urine cytology images

Urine cytology is a test for the detection of high-grade bladder cancer. In clinical practice, the pathologist would manually scan the sample under the microscope to locate atypical and malignant cells. They would assess the morphology of these cells to make a diagnosis. Accurate identification of atypical and malignant cells in urine cytology is a challenging task and is an essential part of identifying different diagnosis with low-risk and high-risk malignancy. Computer-assisted identification of malignancy in urine cytology can be complementary to the clinicians for treatment management and in providing advice for carrying out further tests. In this study, we presented a method for identifying atypical and malignant cells followed by their profiling to predict the risk of diagnosis automatically. For cell detection and classification, we employed two different deep learning-based approaches. Based on the best performing network predictions at the cell level, we identified low-risk and high-risk cases using the count of atypical cells and the total count of atypical and malignant cells. The area under the receiver operating characteristic (ROC) curve shows that a total count of atypical and malignant cells is comparably better at diagnosis as compared to the count of malignant cells only. We obtained area under the ROC curve with the count of malignant cells and the total count of atypical and malignant cells as 0.81 and 0.83, respectively. Our experiments also demonstrate that the digital risk could be a better predictor of the final histopathology-based diagnosis. We also analyzed the variability in annotations at both cell and whole slide image level and also explored the possible inherent rationales behind this variability

Green photosensitisers for the degradation of selected pesticides of high risk in most susceptible food: a safer approach

Pesticides are the leading defence against pests, but their unsafe use reciprocates the pesticide residues in highly susceptible food and is becoming a serious risk for human health. In this study, mint extract and riboflavin were tested as photosensitisers in combination with light irradiation of different frequencies, employed for various time intervals to improve the photo-degradation of deltamethrin (DM) and lambda cyhalothrin (λ-CHT) in cauliflower. Different source of light was studied, either in ultraviolet range (UV-C, 254 nm or UV-A, 320–380 nm) or sunlight simulator (> 380–800 nm). The degradation of the pesticides varied depending on the type of photosensitiser and light source. Photo-degradation of the DM and λ-CHT was enhanced by applying the mint extracts and riboflavin and a more significant degradation was achieved with UV-C than with either UV-A or sunlight, reaching a maximum decrement of the concentration by 67–76%. The light treatments did not significantly affect the in-vitro antioxidant activity of the natural antioxidants in cauliflower. A calculated dietary risk assessment revealed that obvious dietary health hazards of DM and λ-CHT pesticides when sprayed on cauliflower for pest control. The use of green chemical photosensitisers (mint extract and riboflavin) in combination with UV light irradiation represents a novel, sustainable, and safe approach to pesticide reduction in produce

Development and validation of artificial intelligence-based prescreening of large-bowel biopsies taken in the UK and Portugal: a retrospective cohort study

Background Histopathological examination is a crucial step in the diagnosis and treatment of many major diseases. Aiming to facilitate diagnostic decision making and improve the workload of pathologists, we developed an artificial intelligence (AI)-based prescreening tool that analyses whole-slide images (WSIs) of large-bowel biopsies to identify typical, non-neoplastic, and neoplastic biopsies. Methods This retrospective cohort study was conducted with an internal development cohort of slides acquired from a hospital in the UK and three external validation cohorts of WSIs acquired from two hospitals in the UK and one clinical laboratory in Portugal. To learn the differential histological patterns from digitised WSIs of large-bowel biopsy slides, our proposed weakly supervised deep-learning model (Colorectal AI Model for Abnormality Detection [CAIMAN]) used slide-level diagnostic labels and no detailed cell or region-level annotations. The method was developed with an internal development cohort of 5054 biopsy slides from 2080 patients that were labelled with corresponding diagnostic categories assigned by pathologists. The three external validation cohorts, with a total of 1536 slides, were used for independent validation of CAIMAN. Each WSI was classified into one of three classes (ie, typical, atypical non-neoplastic, and atypical neoplastic). Prediction scores of image tiles were aggregated into three prediction scores for the whole slide, one for its likelihood of being typical, one for its likelihood of being non-neoplastic, and one for its likelihood of being neoplastic. The assessment of the external validation cohorts was conducted by the trained and frozen CAIMAN model. To evaluate model performance, we calculated area under the convex hull of the receiver operating characteristic curve (AUROC), area under the precision-recall curve, and specificity compared with our previously published iterative draw and rank sampling (IDaRS) algorithm. We also generated heat maps and saliency maps to analyse and visualise the relationship between the WSI diagnostic labels and spatial features of the tissue microenvironment. The main outcome of this study was the ability of CAIMAN to accurately identify typical and atypical WSIs of colon biopsies, which could potentially facilitate automatic removing of typical biopsies from the diagnostic workload in clinics. Findings A randomly selected subset of all large bowel biopsies was obtained between Jan 1, 2012, and Dec 31, 2017. The AI training, validation, and assessments were done between Jan 1, 2021, and Sept 30, 2022. WSIs with diagnostic labels were collected between Jan 1 and Sept 30, 2022. Our analysis showed no statistically significant differences across prediction scores from CAIMAN for typical and atypical classes based on anatomical sites of the biopsy. At 0·99 sensitivity, CAIMAN (specificity 0·5592) was more accurate than an IDaRS-based weakly supervised WSI-classification pipeline (0·4629) in identifying typical and atypical biopsies on cross-validation in the internal development cohort (p<0·0001). At 0·99 sensitivity, CAIMAN was also more accurate than IDaRS for two external validation cohorts (p<0·0001), but not for a third external validation cohort (p=0·10). CAIMAN provided higher specificity than IDaRS at some high-sensitivity thresholds (0·7763 vs 0·6222 for 0·95 sensitivity, 0·7126 vs 0·5407 for 0·97 sensitivity, and 0·5615 vs 0·3970 for 0·99 sensitivity on one of the external validation cohorts) and showed high classification performance in distinguishing between neoplastic biopsies (AUROC 0·9928, 95% CI 0·9927–0·9929), inflammatory biopsies (0·9658, 0·9655–0·9661), and atypical biopsies (0·9789, 0·9786–0·9792). On the three external validation cohorts, CAIMAN had AUROC values of 0·9431 (95% CI 0·9165–0·9697), 0·9576 (0·9568–0·9584), and 0·9636 (0·9615–0·9657) for the detection of atypical biopsies. Saliency maps supported the representation of disease heterogeneity in model predictions and its association with relevant histological features. Interpretation CAIMAN, with its high sensitivity in detecting atypical large-bowel biopsies, might be a promising improvement in clinical workflow efficiency and diagnostic decision making in prescreening of typical colorectal biopsies. Funding The Pathology Image Data Lake for Analytics, Knowledge and Education Centre of Excellence; the UK Government's Industrial Strategy Challenge Fund; and Innovate UK on behalf of UK Research and Innovation

Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study

Objective To develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis. Design A graph neural network was developed incorporating pathologist domain knowledge to classify 6591 whole-slides images (WSIs) of endoscopic large bowel biopsies from 3291 patients (approximately 54% female, 46% male) as normal or abnormal (non-neoplastic and neoplastic) using clinically driven interpretable features. One UK National Health Service (NHS) site was used for model training and internal validation. External validation was conducted on data from two other NHS sites and one Portuguese site. Results Model training and internal validation were performed on 5054 WSIs of 2080 patients resulting in an area under the curve-receiver operating characteristic (AUC-ROC) of 0.98 (SD=0.004) and AUC-precision-recall (PR) of 0.98 (SD=0.003). The performance of the model, named Interpretable Gland-Graphs using a Neural Aggregator (IGUANA), was consistent in testing over 1537 WSIs of 1211 patients from three independent external datasets with mean AUC-ROC=0.97 (SD=0.007) and AUC-PR=0.97 (SD=0.005). At a high sensitivity threshold of 99%, the proposed model can reduce the number of normal slides to be reviewed by a pathologist by approximately 55%. IGUANA also provides an explainable output highlighting potential abnormalities in a WSI in the form of a heatmap as well as numerical values associating the model prediction with various histological features. Conclusion The model achieved consistently high accuracy showing its potential in optimising increasingly scarce pathologist resources. Explainable predictions can guide pathologists in their diagnostic decision-making and help boost their confidence in the algorithm, paving the way for its future clinical adoption

Efficacy of Reduced Osmolarity Oral Rehydration Solution in Children with Acute Diarrhoea

Background: To compare the efficacy of reduced osmolarity ORS with standard WHO ORS in children with acute diarrhoea.Methods: In this comparative study 1080 children suffering from acute diarrhoea were selected from emergency. Children were divided randomly into 2 groups. Group A was given reduced osmolarity ORS and Group B was given standard WHO ORS. Proforma was filled at the time of admission and after every four hours till there was no dehydration. Criteria of treatment efficacy was, need of unscheduled IV infusions, number of stools, improvement in number of episodes of vomiting and duration of hospital stay in hours.Results: A total of 1080 children with acute diarrhoea were included in the study. 540 children in group A were given reduced osmolarity ORS and 540 children in group B were given WHO ORS. Efficacy of treatment was significantly good in group A (75%) as compared to group B (34%). Unscheduled IV infusion was less in group A as compared to group B. Number of stools were significantly reduced in group A as compared to group B. Vomiting during rehydration was also lesser in group A as compared to group B. Patients in group A had significantly reduced average duration of hospital stay as compared to group B.Conclusion: Reduced osmolarity ORS reduced the duration and severity of symptoms in children with acute diarrhea and treatment was well tolerated with no side effects