Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification

In recent times, DFU (diabetic foot ulcer) has become a universal health problem that affects many diabetes patients severely. DFU requires immediate proper treatment to avert amputation. Clinical examination of DFU is a tedious process and complex in nature. Concurrently, DL (deep learning) methodologies can show prominent outcomes in the classification of DFU because of their efficient learning capacity. Though traditional systems have tried using DL-based models to procure better performance, there is room for enhancement in accuracy. Therefore, the present study uses the AWSg-CNN (Adaptive Weighted Sub-gradient Convolutional Neural Network) method to classify DFU. A DFUC dataset is considered, and several processes are involved in the present study. Initially, the proposed method starts with pre-processing, excluding inconsistent and missing data, to enhance dataset quality and accuracy. Further, for classification, the proposed method utilizes the process of RIW (random initialization of weights) and log softmax with the ASGO (Adaptive Sub-gradient Optimizer) for effective performance. In this process, RIW efficiently learns the shift of feature space between the convolutional layers. To evade the underflow of gradients, the log softmax function is used. When logging softmax with the ASGO is used for the activation function, the gradient steps are controlled. An adaptive modification of the proximal function simplifies the learning rate significantly, and optimal proximal functions are produced. Due to such merits, the proposed method can perform better classification. The predicted results are displayed on the webpage through the HTML, CSS, and Flask frameworks. The effectiveness of the proposed system is evaluated with accuracy, recall, F1-score, and precision to confirm its effectual performance

Ensemble deep learning for brain tumor detection

With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.Qatar National Library and Qatar university internal - grant No. IRCC-2021-01

DataPype: A Fully Automated Unified Software Platform for Computer-Aided Drug Design

With the advent of computer-aided drug design (CADD), traditional physical testing of thousands of molecules has now been replaced by target-focused drug discovery, where potentially bioactive molecules are predicted by computer software before their physical synthesis. However, despite being a significant breakthrough, CADD still faces various limitations and challenges. The increasing availability of data on small molecules has created a need to streamline the sourcing of data from different databases and automate the processing and cleaning of data into a form that can be used by multiple CADD software applications. Several standalone software packages are available to aid the drug designer, each with its own specific application, requiring specialized knowledge and expertise for optimal use. These applications require their own input and output files, making it a challenge for nonexpert users or multidisciplinary discovery teams. Here, we have developed a new software platform called DataPype, which wraps around these different software packages. It provides a unified automated workflow to search for hit compounds using specialist software. Additionally, multiple virtual screening packages can be used in the one workflow, and if different ways of looking at potential hit compounds all predict the same set of molecules, we have higher confidence that we should make or purchase and test the molecules. Importantly, DataPype can run on computer servers, speeding up the virtual screening for new compounds. Combining access to multiple CADD tools within one interface will enhance the early stage of drug discovery, increase usability, and enable the use of parallel computing

High Thyroid Cancer Incidence Rate in a Community near a Landfill: A Descriptive Epidemiological Assessment

Background: to investigate the high thyroid cancer incidence rate of Staten Island and to disentangle the effects of potential environmental exposure from a landfill from screening. Methods: age-adjusted thyroid cancer incidence rates obtained from the New York State Public Access Cancer Epidemiology Data for New York State (NYS) excluding New York City (NYC) and the five NYC boroughs, including Staten Island, were mapped over time (1995–2018), investigated per age group and by percentage of localized thyroid cancer. Changes in trends were assessed using joinpoint. Contaminants of concern on Staten Island were assessed for carcinogenic and endocrine disruptive properties. Results: a more pronounced thyroid cancer incidence rate increase, without a difference in age distribution and similar percentages of localized thyroid cancer, was found in Staten Island compared to its demographic equivalent (NYS excluding NYC). Multiple contaminants of concern with carcinogenic and endocrine disrupting properties (e.g., cadmium, lead) were identified in air, water and sediment samples. Conclusion: investigations into the effects of increased/sustained environmental exposures are needed in chronically exposed populations to identify potential mechanisms of action of certain pollutants

An efficient approach for textual data classification using deep learning

Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. Similarly, deep learning offers enormous benefits for text classification since they execute highly accurately with lower-level engineering and processing. This paper employs machine and deep learning techniques to classify textual data. Textual data contains much useless information that must be pre-processed. We clean the data, impute missing values, and eliminate the repeated columns. Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. Results reveal that LSTM achieves 92% accuracy outperforming all other model and baseline studies.Qatar University [IRCC-2021-010]

Synthesis, Characterisation and Mechanism of Action of Anticancer 3-Fluoroazetidin-2-ones

The stilbene combretastatin A-4 (CA-4) is a potent microtubule-disrupting agent interacting at the colchicine-binding site of tubulin. In the present work, the synthesis, characterisation and mechanism of action of a series of 3-fluoro and 3,3-difluoro substituted β-lactams as analogues of the tubulin-targeting agent CA-4 are described. The synthesis was achieved by a convenient microwave-assisted Reformatsky reaction and is the first report of 3-fluoro and 3,3-difluoro β-lactams as CA-4 analogues. The β-lactam compounds 3-fluoro-4-(3-hydroxy-4-methoxyphenyl)-1-(3,4,5-trimethoxy phenyl)azetidin-2-one 32 and 3-fluoro-4-(3-fluoro-4-methoxyphenyl)-1-(3,4,5-trimethoxyphenyl)azetidin-2-one) 33 exhibited potent activity in MCF-7 human breast cancer cells with IC50 values of 0.075 µM and 0.095 µM, respectively, and demonstrated low toxicity in non-cancerous cells. Compound 32 also demonstrated significant antiproliferative activity at nanomolar concentrations in the triple-negative breast cancer cell line Hs578T (IC50 0.033 μM), together with potency in the invasive isogenic subclone Hs578Ts(i)8 (IC50 = 0.065 μM), while 33 was also effective in MDA-MB-231 cells (IC50 0.620 μM). Mechanistic studies demonstrated that 33 inhibited tubulin polymerisation, induced apoptosis in MCF-7 cells, and induced a downregulation in the expression of anti-apoptotic Bcl2 and survivin with corresponding upregulation in the expression of pro-apoptotic Bax. In silico studies indicated the interaction of the compounds with the colchicine-binding site, demonstrating the potential for further developing novel cancer therapeutics as microtubule-targeting agents

Out‐of‐pocket costs associated with head and neck cancer treatment

Abstract Background Out‐of‐pocket costs (OOPC) associated with treatment have significant implications on quality of life and survival in cancer patients. Head and neck cancer patients face unique treatment‐related challenges, but to date OOPC have been understudied in this population. Aims This study aims to identify and measure OOPC for patients with head and neck cancer (HNC) in Ontario. Methods HNC patients between 2015 and 2018 at Princess Margaret Cancer Centre in Toronto were recruited. Participants completed OOPC questionnaires and lost income questions during radiation, post‐surgery, and 3, 6, 12, and 24 months after completion of treatment. Associations between OOPC and treatment modality and disease site were tested with multivariable hurdle regression. Results A total of 1545 questionnaires were completed by 657 patients. Median estimated OOPC for the total duration of treatment for participants undergoing chemoradiation was $1452 [$0–14 616], for surgery with adjuvant radiation or chemoradiation (C/RT) was $1626, for radiation therapy alone was$635, and for surgery alone was $360. The major expenses for participants at the mid‐treatment time‐point was travel (mean$424, standard error of the mean [SEM] $34) and meals, parking, and accommodations (mean$617, SEM $67). In multivariable analysis, chemoradiation, surgery with C/RT, and radiation were associated with significantly higher OOPC than surgery alone during treatment (791% higher, p < .001; 539% higher, p < .001; 370% higher, p < .001 respectively) among patients with non‐zero OOPC. Participants with non‐zero OOPC in the laryngeal cancer group paid 49% lower OOPC than those with oropharyngeal cancers in adjusted analysis (p = .025). Conclusions Patients undergoing treatment for HNC pay significant OOPC. These costs are highest during treatment and gradually decrease over time. OOPC vary by patient demographics, clinical factors, and, in particular, treatment modality

WPOI-5: Accurately Identified at Intraoperative Consultation and Predictive of Occult Cervical Metastases

BACKGROUND: Frozen section analysis of oral cancer specimens is ideal for assessing margin distances and depth of invasion (DOI); the latter impacts intraoperative decisions regarding elective neck dissection (END). Here, we show that intraoperative determination of worst pattern of invasion (WPOI), specifically WPOI-5, has a high level of accuracy. This relates to our demonstration herein that WPOI-5 predicts occult cervical metastases (OCM) for pT1 oral squamous carcinoma (OSC). METHODS: The presence of OCM was correlated with WPOI in 228 patients with primary T1/T2/cN0 OSC undergoing resection and END. Concordance between intraoperative and final pathology WPOI determination was assessed on 51 cases of OSC. RESULTS: WPOI-5 predicts OCM in pT1 patients, compared with WPOI-4/WPOI-3 (p \u3c 0.0001). Most pT1 WPOI-5 tumors had DOI of 4-5 mm (24/59 or 40.7%). Only two pT1 WPOI-5 tumors had DOI \u3c 4 mm (3.0 and 3.5 mm). If END were performed in this pT1 cohort for all WPOI-5 OSC patients regardless of DOI, OR all OSC patients with DOI ≥ 4 mm regardless of WPOI, then no OCM would be missed (p = 0.017, 100% sensitivity, 29% specificity, 77% positive predictive value, 23% negative predictive value). With respect to intraoperative WPOI-5 determination, the accuracy, sensitivity, and specificity was 92.16, 73.33, and 100.0%, respectively. CONCLUSIONS: DOI ≥ 4 mm is the dominant predictor of OCM. For the rare WPOI-5 OSC with DOI \u3c 4 mm, it is reasonable to suggest that surgeons perform END. WPOI-5 may be accurately determined intraoperatively. As microscopic instruction is needed to accurately assess WPOI-5, a teaching link is included in this manuscript