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In time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.

In this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.

The proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.

Research in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.

The proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

The present research work is carried out for determining haemoprotozoan diseases in cattle and breast cancer diseases in humans at early stage. The combination of LeNet and bidirectional long short-term memory (Bi-LSTM) model is used for the classification of heamoprotazoan samples into three classes such as theileriosis, babesiosis and anaplasmosis. Also, BreaKHis dataset image samples are classified into two major classes as malignant and benign. The hyperparameter optimization is used for selecting the prominent features. The main objective of this approach is to overcome the manual identification and classification of samples into different haemoprotozoan diseases in cattle. The traditional laboratory approach of identification is time-consuming and requires human expertise. The proposed methodology will help to identify and classify the heamoprotozoan disease in early stage without much of human involvement.

LeNet-based Bi-LSTM model is used for the classification of pathology images into babesiosis, anaplasmosis, theileriosis and breast images classified into malignant or benign. An optimization-based super pixel clustering algorithm is used for segmentation once the normalization of histopathology images is conducted. The edge information in the normalized images is considered for identifying the irregular shape regions of images, which are structurally meaningful. Also, it is compared with another segmentation approach circular Hough Transform (CHT). The CHT is used to separate the nuclei from non-nuclei. The Canny edge detection and gaussian filter is used for extracting the edges before sending to CHT.

The existing methods such as artificial neural network (ANN), convolution neural network (CNN), recurrent neural network (RNN), LSTM and Bi-LSTM model have been compared with the proposed hyperparameter optimization approach with LeNET and Bi-LSTM. The results obtained by the proposed hyperparameter optimization-Bi-LSTM model showed the accuracy of 98.99% when compared to existing models like Ensemble of Deep Learning Models of 95.29% and Modified ReliefF Algorithm of 95.94%.

In contrast to earlier research done using Modified ReliefF, the suggested LeNet with Bi-LSTM model, there is an improvement in accuracy, precision and F-score significantly. The real time data set is used for the heamoprotozoan disease samples. Also, for anaplasmosis and babesiosis, the second set of datasets were used which are coloured datasets obtained by adding a chemical acetone and stain.

The purpose of this study is to examine the acute toxicity and antidiabetic activity of Asystacia gangetica leaf ethanol extract.

The study was designed as completely randomized in vivo experimental model. Where acute toxicity study was carried out using 30 albino mice, randomly assigned into six groups of five mice each. Toxicity signs and mortality were observed in the rats within a period of 24 h. The acute and sub-acute antidiabetic study was carried out using 50 rats, randomly assigned into five groups of 10 rats each. The rats’ blood glucose levels were determined and used to assess the acute and sub-acute antidiabetic activity of the extract.

Results obtained from the acute toxicity study indicated no death in any of the study groups, even at 5,000 mg/kg body weight and showed no signs of toxicity. The acute antidiabetic study showed that treatment with 400 mg/kg of the extract significantly (p = 0.01) lowered glucose level in the diabetic rats from 430.6 to 177.4 mg/dl while 800 mg/kg brought down glucose level from 370 to 144.2 mg/dl by the end of 6 h following administration when compared with the diabetic control group. It was observed that the effect of the extract mostly at 800 mg/kg also compared favorably with that of the standard drug (glibenclamide), which lowered glucose level in diabetic rats from 374.2 to 176.4 mg/dl. Furthermore, the significant reduction was evident from 4, 2 and 2 h for 400 mg/kg extract, 800 mg/kg extract and 5 mg/kg glibenclamide, respectively. At sub-acute level the blood glucose was lowered from 155.6 to 127.2 mg/dl, 137 to 124.4 mg/dl and 151.8 to 121.8 mg/dl for diabetic rats treated with 400 mg/kg, 800 mg/kg and 5 mg/kg glibenclamide, respectively, when compared to the diabetic untreated rats, which ranged from 417.6 to 358.6 mg/dl. The biochemical profile, lipid profile and hematological examination were all positively restored near to normal with the herbal treatment at the different doses. At histopathology level, the liver of the rats treated with 400 mg/kg had moderate portal inflammation without interface or lobular hepatitis while that of 800 mg/kg showed severe portal inflammation with the interface and lobular hepatitis with extensive confluents necrosis. The pancreatic cells of the treated rat showed no significant difference in the β-cells of the islets of Langerhans with hyperplasia of the acinar cell when compared to the diabetic untreated.

The record of no death and signs of toxicity implies that the extract is safe for consumption even at a high dosage of 5,000 mg/kg body weight. The significant (p = 0.01) reduction in the plasma glucose level of the treated rats as compared to the control is an indication of blood glucose-lowering potential of the extract at two different doses. The significant reduction evident of the extract at different hours and days for the two doses implies that the extract rate of lowering potentials is dose-dependent. The evidence of moderate-severe portal inflammation with the interface and lobular hepatitis at 800 mg/kg treatment is an indication that the intake of this herb at high dosage for long period is not safe for the liver tissue.

The outcome of this study suggested that the Asystacia gangetica should also be used as a vegetable in-home food preparation and food processing to use its antidiabetic effect. The herbal extract could also be incorporated into a food product and processed into herbal tea bag for convenient. The subjection of this herbal plant to heat treatment during processing could be a possible avenue to make it safe.

The economic burden and complications of diabetes mellitus management will be reduced if the practical implication of this research finding is implemented in foods as vegetable and in functional food production.

This study revealed that Asystacia gangetica leaf extract may be safe and effective for use at a low dose for acute management of diabetes mellitus. This research may be of value to those living with diabetes mellitus.

Polyphenols possess anti-allergic activities. Catechin is one of the polyphenols that are abundantly present in the Acacia catechu. In this study, the authors investigated the effect of catechin isolated from A. catechu in an experimental mouse model of ovalbumin (OVA)-induced allergic asthma.

Catechin was isolated from A. catechu, and phytochemical analysis was carried out by ultraviolet visible and thin-layer chromatography (TLC), high pressure thin-layer chromatography was used for the determination of an amount of catechin present. In a first set of an experiment, the authors have carried out dose-dependent evaluation of catechin on histamine synthesis in normal rats. In another study, allergic asthma was induced in BALB/c mice by intraperitoneal injection of 50 mg OVA dissolved in 4 mg aluminum hydroxide dissolved in 0.2 ml saline on Days 0 and 14. Catechin was given orally at the dose of 100 mg/kg, once a day from Day 1 to Day 35 and after which various respiratory parameters such as tidal volume, respiratory rate and airflow rate, biochemical parameters such as histamine release from mast cells, bronchoalveolar (BAL) lavage fluid analysis and histopathology of lungs were carried out.

Catechin showed significant (p < 0.05) improvement in respiratory parameters such as tidal volume, respiratory rate and airflow rate, as well as biochemical and hematological parameters such as blood histamine, serum bicarbonate and nitric oxide levels as compared to the disease control group. The treatment also showed inhibitory effects on histamine synthesis in rat peritoneal as well as BAL mast cells. Also, a significant (p < 0.05) improvement in lung histopathology was observed with catechin.

From the present study, the authors can conclude that catechin exhibited potent anti-allergic activity by inhibition of histamine synthesis by inhibition of histidine decarboxylase enzyme. The study suggests that catechin has therapeutic potential for the treatment of allergic inflammatory disease in humans.

Histopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.

Three pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.

This study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.

The best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.

India's diagnostics business valued at USD 10 billion was growing at 20% annually. Several players with different business models competed. Dr. Lal PathLabs, the world's largest histopathology centre led with a menu of 3,500 tests, 1,600 collection centres and 7,000 pick-up points. Its Initial Public Offer had been oversubscribed 33.41 times and the team at Dr. Lal PathLabs was excited about expanding its international footprint. Two overseas companies were incorporated in Netherlands and Nepal. Yet, there were enormous unmet needs in India alongside potential for public-private partnerships. Trade-offs over portfolio choice and regional versus international footprint needed thinking through.

Renal failure is an end-stage consequence after persistent hyperglycemia during diabetic nephropathy (DN), and the etiology of DN has been linked to oxidative stress. The purpose of this research was to determine the beneficial synergistic effects of S-Allyl Cysteine (SAC) and Taurine (TAU) on oxidative damage in the kidneys of type 2 diabetic rats induced by hyperglycemia.

Experimental diabetes was developed by administering intraperitoneal single dose of streptozotocin (STZ; 65 mg/kg) with nicotinamide (NA; 230 mg/kg) in adult rats. Diabetic and control rats were treated with SAC (150 mg/kg), TAU (200 mg/kg) or SAC and TAU combination (75 + 100 mg/kg) for four weeks. The estimation of body weight, fasting blood glucose (FBG), oral glucose tolerance test (OGTT), oxidative stress markers along with kidney histopathology was done to investigate the antidiabetic potential of SAC/TAU in the NA/STZ diabetic group.

The following results were obtained for the therapeutic efficacy of SAC/TAU: decrease in blood glucose level, decreased level of thiobarbituric acid reactive substances (TBARS) and increased levels of GSH, glutathione-s-transferase (GST) and catalase (CAT). SAC/TAU significantly modulated diabetes-induced histological changes in the kidney of rats.

SAC/TAU combination therapy modulated the oxidative stress markers in the kidney in diabetic rat model and also prevented oxidative damage as observed through histopathological findings.

The purpose of this study is to evaluate the effects of prebiotic, probiotic and synbiotic supplementation on liver histopathology and TLR-4, NFκB and TNF-α gene expression involved in the inflammatory cascade and pathogenesis of experimental nonalcoholic fatty liver disease (NAFLD).

Wistar male adult rats (n = 40) were submitted to hypercholesterolemic conditions (60 days). On Day 30 of hypercholesterolemic conditions, rats were subdivided in five groups: negative control (NC), positive control (PC), prebiotic (PRE), probiotic (PRO) and synbiotic (SYN). All rats were sacrificed on Day 60. Liver tissue was used to verify histopathological changes and gene expression. Gene expression of TLR-4, TNF-α and NFκB was evaluated in liver tissue using RT-qPCR.

Histopathological analysis: PC showed more changes than NC, and PRE and SYN showed fewer alterations than PC. Gene expression analysis: PRE showed higher TLR-4, and NFκB and TNF-α compared to PC. Also, PRE showed higher TLR-4 when compared to PRO and SYN. SYN group revealed higher TLR-4 and NFκB expressions compared to PC. PRO group also showed higher NFκB expression compared to PC.

NAFLD is a significant health concern, and it found that prebiotic, probiotic and synbiotic supplementation could have positive effects as a nonpharmacological approach to control this disease.

This study aims to analyze the pattern of deaths in detention in Northern Tunisia as well as the causes of death.

The authors conducted a cross-sectional retrospective study including all the casualties of death in detention examined in the legal medicine Department in the main teaching hospital from 2005 to 2019. The department covers 10 out of the 11 governorates of Northern Tunisia and 13 prisons.

Of a total of 197 casualties, only 2 were females. The mean age was 45.39 ± 14.43 years. A known medical history was reported in 63.5%, mainly cardiovascular disease, mental health disorders and diabetes. Half of the deaths occurred at the hospital. A total of 53 victims spent less than one year in custody before their death. Most deaths occurred due to disease-related causes (78.7%; n = 155); among these, 69 victims died from cardiovascular disease. Suicide accounted for 3.6% of the casualties and homicides for four cases.

Several missing data regarding the details of the detention circumstances as well as the absence in some cases of the toxicological and histopathology analysis results, which could bias the study findings.

Death in detention in Northern Tunisia involved mainly males between their 30s and their 50s who died mainly from cardiovascular or pulmonary disease. These results underscore the importance of empowering the penitentiary health system.

To the best of the authors’ knowledge, this study is one of largest studies with regard to the number of decedents and the number of prisons from the Arab countries allowing to draw a pattern of casualties of death in prison.

Presents the results of a study which was undertaken to establish best practice in the management of a pathology department through best practice. The study was carried out in two stages: an in‐depth analysis of the pathology department through a SWOT analysis and in‐depth interviews with 25 key internal and external customers, and a survey of NHS laboratories both in the UK and Ireland by targeting a sample of consultant pathologists in 50 random locations – the purpose of this questionnaire was an attempt at establishing best practice in pathology quality management. The study revealed that in pathology there is currently a major dependency on quality control that reflects a compliance to set standards laid down by professional bodies. It also identified a poor understanding of customer needs and expectations, a mechanistic culture which is resistant to change, and a reluctance by pathology managers to delegate ownership for ongoing quality improvement or to take responsibility for quality improvement issues such as waste reduction and cost improvement. Concludes with a set of recommendations geared towards helping managers of pathology services to deal with issues such as cost of quality, listening to customers, empowering employees and getting the job done right the first time and every time.