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Breast cancer is the most commonly found cancer in India and the world. As per Global Cancer Observatory, in 2020 this cancer alone was the reason for the death of more than 6.8 million women throughout the world. There is no single cause of breast cancer but it could be a combination of one's environment, one's genes and the way one lives her life. These breast tumors can be benign (non- cancerous) or malignant (potentially-cancerous). Therefore, it becomes essential to identify them properly for appropriate treatment. Although histopathology images are used to judge breast cancer, there is always a chance of human error. This paper aims to simplify the breast cancer classification process for everyone. We propose a deep learning based Convolutional Neural Network model to detect and classify the breast cancer using histopathology images. This method uses pretrained neural network DenseNet-201 for features extraction from the images, which then used to predict for classification. Our model reached an accuracy of 97.05%.

Aims: We carried out a systematic literature review and meta-analytic synthesis to find out association between DM and related outcomes in patients with COVID-19 infection. Methods: We systematically searched MEDLINE, and Web of Science to identify studies investigating comorbidities, clinical manifestations and resource utilization of diabetic patients exposed with COVID-19 published from inception to January 2021. Meta-analysis was carried out using Review Manager 5.3. Random effects model was used to compute the pooled estimates of odds ratio/mean difference (OR)/(MD) and 95% confidence intervals (CI). Results: Results from the pooled meta-analysis found that CVD, hypertension, AKI, cerebrovascular disease, AKI and ARDS were significantly associated with DM in COVID-19 infected patients compared to non-diabetic patients. There is significant association found between mortality and DM compared to non-diabetic patients [OR (95% CI): 2.46 (1.68, 3.58)]. ICU admission and use of mechanical ventilation was significantly associated with DM and COVID-19 vs. non-diabetic [OR (95% CI): 2.79 (1.79,4.34) and 3.33 (2.05, 5.42)] respectively. However, LOS, hospitalization, and ICU admission were not significantly differing between diabetes vs. non-diabetes. Conclusions: The results showed a significant association between mortality and DM exposed with COVID-19. Other co-morbidities especially CVD/hypertension could be a serious threat for DM COVID-19 infected patients for the higher mortality.

Objective: There is lack of evidence on the burden of and factors associated with non-adherence to anti-diabetic medication among individuals living with diabetes in low-and middle-income countries (LMICs). Therefore, we carried out a systematic literature review and meta-analytic synthesis to estimate non-adherence to anti-diabetic medication reported among adults in LMICs and to explore factors affecting non-adherence. Methods: We systematically searched MEDLINE and Embase to identify studies investigating non-adherence to anti-diabetic medications published between January 2000 and May 2020. Cross-sectional studies that had been conducted among individuals with diabetes in LMICs were eligible for the selection process. Critical appraisal of the included studies was carried out using the Newcastle Ottawa Scale. Meta-analysis was carried out using Stata 14.2. Random effects model was used to compute the pooled proportion at 95% confidence interval. Results: Forty-three studies met the inclusion criteria, of which 13 studies were used in meta-analysis. The pooled proportion of non-adherence to anti-diabetic medications using the eight-item Morisky Medication Adherence Scale (MMAS) was 43.4% (95% CI: 17.5–69.4; p=0.000) and 29.1% (95% CI: 19.8–38.4; p=0.000) when using the cut-off at 80 or 90%. The pooled proportion of non-adherence was 29.5% (95% CI: 25.5–33.5; p=0.098) when using the four-item Morisky Medication Adherence Scale. The factors for non-adherence based on World Health Organization demonstrated considerable variation of non-adherence to ant-diabetic medication in LMICs depending on the methods used to estimate non-adherence. Conclusions: These findings demonstrate a significantly higher proportion of medication non-adherence among individuals with diabetes in LMIC settings when MMAS-8 item scale was used and low when 80-90% cut-off scales were used. Various factors, such as disease factors, therapy-related factors, healthcare system factor, patient-centered factors, and social and economic factors contributed to non-adherence. Therefore, comprehensive multifaceted strategies are urgently needed to address factors associated with anti-diabetic medication non-adherence.

Background and objective There is a lack of updated data regarding pediatric cancer in Kuwait, with no separate childhood cancer registry available in the country prior to this study. We aimed to determine the trends in different cancer types with respect to international statistics, in order to understand their nature and identify gaps in their diagnosis and management. Methods This retrospective study was based on data obtained from the first national childhood cancer registry of the National Bank of Kuwait (NBK) Specialized Hospital for Children, the only hospital that manages pediatric cancer patients in Kuwait. The registry included the data of all children with cancer in Kuwait from 2004 to 2017 and had complete data files. Findings The total number of patients in the childhood cancer registry was 1,387. A total of 1,009 files met our eligibility criteria. In 2017, the incidence of childhood leukemia was determined to be six per 100,000 people, and for other cancers, it was 12.6 per 100,000 people. The most common cancer was leukemia (457 patients), followed by lymphoma (141 patients), and brain tumors (92 patients). The majority of children received chemotherapy (n=891, 88.3%). Post induction or treatment, the majority of patients achieved complete remission (n=790, 78.3%). The overall survival rate of children with cancer was around 80%. Major complications developed in 9% of patients. Conclusion These findings highlight the importance of hospital-based cancer registries. Active data management programs are essential to monitor outcomes, measure the effectiveness of current practice, and improve the quality of care.

Background Non-adherence to anti-diabetic medication is an important cause of uncontrolled blood glucose that leads to complications of diabetes. However, there is lack of evidence on the burden of and factors associated with non-adherence to anti-diabetic medication among individuals living with diabetes in low-and middle-income countries (LMICs). Objectives This systematic literature review and meta-analytic synthesis aims to estimate non-adherence to anti-diabetic medication reported among adults in LMICs and explores factors affecting non-adherence. Methods We systematically searched MEDLINE and Embase to identify studies investigating non-adherence to anti-diabetic medications published between January 2000 and May 2020. Two authors carried out study selection, screening, and data extraction independently. Cross-sectional studies that had been conducted among individuals with diabetes in LMICs were eligible for the selection process. Critical appraisal of the included studies was carried out using the Newcastle Ottawa Scale. Meta-analysis was carried out using Stata 14.2. Random effects model was used to compute the pooled proportion at 95% confidence interval. Results Forty-three studies met the inclusion criteria, of which 13 studies were used in meta-analysis. The pooled proportion of non-adherence to anti-diabetic medications using the eight-item Morisky Medication Adherence Scale (MMAS) was 43.4% (95% CI: 17.5-69.4; p=0.000) and 29.1% (95% CI: 19.8-38.4; p=0.000) when using the cut-off at 80 or 90%. The pooled proportion of non-adherence was 29.5% (95% CI: 25.5-33.5; p=0.098) when using the four-item Morisky Medication Adherence Scale. The factors for non-adherence based on World Health Organization demonstrated considerable variation of non-adherence to ant-diabetic medication in LMICs depending on the methods used to estimate non-adherence. Conclusions These findings demonstrate a significantly higher proportion of medication non-adherence among individuals with diabetes in LMIC settings when MMAS-8 item scale was used and low when 80-90% cut-off scales were used. Various factors, such as disease factors, therapy-related factors, healthcare system factor, patient-centered factors, and social and economic factors contributed to non-adherence. Therefore, comprehensive multifaceted strategies are urgently needed to address factors associated with anti-diabetic medication non-adherence.

Achieving high power conversion efficiency (PCE) polymer solar cells (PSCs) has been very challenging and the ultimate goal for their commercialization. Precise investigation of the active layer morphology and newer methods to process these layers has been a crucial factor to realize high PCE. There is also a rising demand for the fabrication of PSCs with a thick active layer over a large area to facilitate their commercial application. However, to achieve higher thickness it has been observed that the active layer film morphology is compromised, which in turn reduces the device performance. Many device engineering techniques have been attempted by researchers to obtain the best possible morphology for enabling better charge transport and hence improved device efficiency. Reports of achieving thick active layer PSCs along with regulation of the morphology in favour of charge transport have also appeared in the literature. This review aims to cover important device engineering techniques which have been reported in the literature for obtaining high performance PSCs by modifying the morphology and those which have been targeted to achieve thick active layers for the same. Besides including general device engineering aspects, a discussion on varying the donor (D) and acceptor (A) ratio and thermal annealing for regulating the active layer is also included. Thereafter, solvent/additive engineering and materials engineering have been discussed to understand their role in the active layer morphology and fabrication of thick film solar cells. Lastly, all the advanced device engineering methods such as solvent vapor annealing, doctor blading, and hot-casting for the fabrication of high-performance PSCs have been covered. This review will be a good tool for all researchers who are considering various device engineering methods to obtain high performance thick PSCs.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC)0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Dhaiphul (Woodfordia fruticosa) is a frequently demanded plant in South-East Asian regions for its diverse medicinal values. This study was proposed to examine antioxidant, antidiabetic, and antidepressant potentials of methanol extract of W. fruticosa leaves (MEWF) and its derived n-hexane (NHFMEWF) and ethyl acetate (EAFMEWF) fractions through in vitro, in vivo, and computational models. Among test samples, MEWF and EAFMEWF contained the highest phenolic content and showed maximal antioxidant activity in DPPH radical scavenging and ferric reducing power assays. In comparison, NHFMEWF possessed maximum flavonoid content and a significantly potent α-amylase inhibitory profile comparable with positive control acarbose. In animal models of depression (forced swimming and tail suspension test), EAFMEWF and NHFMEWF demonstrated a dose-dependent antidepressant-like effect, explicitly, the depressive-like behaviors significantly declined in EAFMEWF-treated dosing groups in contrast to the control group. In the computational analysis, previously isolated flavonoid compounds from Dhaiphul leaves manifested potent binding affinity against several key therapeutic target proteins of diabetes and depressive disorders including α-amylase, serotonin transporter, dopamine transporter, and neuronal nitric oxide synthase with varying pharmacokinetics and toxicity profiles. This research’s outcomes may provide potential dietary supplements for mitigating hyperglycemia, cellular toxicity, and depressive disorder.

The permeability of the soil is one of the most important properties of an unlined earthen canal or river bed. Using fine plastic particles has experimentally proven to reduce soil permeability, but the experimental study of the effect of a variety of types of plastic fines and their percentages in riverbed soil is tedious work to do. Estimation of permeability of riverbed soil by altering it with plastic fines using Artificial Neural Networks (ANNs) may reduce this effort. Particle size distributions (PSDs) have a significant influence on the permeability of bed soils. Being able to predict the permeability of bed soil by knowing the PSDs may provide an easy approach to know the loss of water by percolation. This study has investigated the quantitative relationships between permeability and PSD indices using ANNs. The aim was to build a mathematical model capable of predicting the permeability of bed soil by PSD indices of choice. A model was built using ANNs including PSD indices as input and permeability as output. The model stated that the coefficients of curvature and uniformity (Cc) and (Cu) and effective particle size (D50) may be used to predict the bed permeability. The computational model was able to predict the effect of variation of PSD indices on bed permeability, thus allowing increasing the efficiency of the river bed, to ensure maximum downstream water supply, lesser seepage and percolation and better productivity. The test result has confirmed the efficiency of the developed ANN tool in predicting the bed permeability for different PSD combinations.

Background: Onychopathies or nail disorders are associated with social stigma and causes limitation of daily activities by hampering the function of both fingers and toes. Aim: To evaluate the impact of onychopathies on quality of life (QoL) and compare the severity of impact on QoL in various nail disorders. Materials and Methods: A hospital-based cross-sectional study consisting of 540 patients with onychopathies was conducted in the dermatology outpatient department. Patients were requested to complete a nail-specific QoL questionnaire consisting of 24 and 16 questions, respectively, for fingernails (group F) and toenails (group T) with five possible responses to each question. A score of 1-5 was given to each response. Statistical analysis was done to compare the impact of QoL on the different types of onychopathies. Results: We found that onychopathies have a significant impact on QoL. QoL was significantly more affected when multiple nails were involved (P = 0.020 for group F and P = 0.001 for group T). QoL impact was statistically more significant in women (P = 0.038 for group F and P < 0.001 for group T) and in younger people aged

This paper presents an experimental investigation carried out to understand the behavior of reinforced concrete sandwiched panels (RCSPs) under blast load. An RCSP is composed of an EPS (Expanded Polystyrene) foam core, which is surrounded by spray-on reinforced concrete skins (like ferrocement overlay) on both sides. For this purpose blast load test on four free-standing RCSPs in 17 blast case scenarios were conducted and the physical behavior was analyzed through high quality visuals and fragility curves were developed. The fragility curve interprets the relationship between damage intensity with charge size and scaled of distance. On the basis of results it is concluded that RCSP panel has greater capability of absorbing and dissipating energy generated by blast, and stable against fragmentation as compared to other conventional building systems used in Pakistan. Based on the assessment RCSPs walls/panels are strongly recommended for protective barrier installed in the vicinity of critical infrastructure against blast loading.

Background: Omega-3 fatty acids have been reported to reduce disease severity in psoriasis in various studies. This study aims to study the effects of omega-3 fatty acid supplementation on the quality of life in psoriasis. Methods: This open, non blinded interventional study divided patients of psoriasis with affected body surface area of less than

Background: Attention-deficit hyperactivity disorder (ADHD) is a widespread and debilitating disorder with relatively high prevalence in Saudi Arabia. Neuropsychological and radiological investigations have revealed that there are some differences in the components of the brain regions in children with and without ADHD. In this study we have performed whole exome sequencing (WES) in four non-familial cases of ADHD from Makkah Region to identify the genetic polymorphisms associated with the disease in our Saudi population. Methods: Exome sequencing was carried out using Ion Proton with AmpliSeq Exome library methods, and the data were analysed by Ion Reporter 5.6 software. Results: A total of 33 variants were identified from 222 genes selected from the GWAS catalogue for ADHD associated genes. However, the SNPs we identified in these genes were not reported to be associated with ADHD in previous studies. We have identified 2 novel missense variants; one in c.3451G > T; p. (Ala1151Ser) in ITGA1 gene and another is c.988G > A; p. (Ala330Thr) in SPATA13 genes. The variants rs928661, rs11150370 and rs386792899 were the only three variants that appeared on all the 4 patients studied. Six missense variants, rs16841277, rs2228209, rs2230283, rs3741883, rs1716 and rs2272606, were found in 3 different patients, respectively. However, the three documented variants are rs13166360 with bipolar disorder, rs920829 with neuropathic pain, and rs6558702 with schizophrenia. Conclusion: We have identified 2 novel variants in ADHD children. SIFT score of all variants indicates that these substitutions have damaging effects on the protein function. Further screening studies are recommended for confirmation.

BACKGROUND Seborrheic keratosis is a benign skin tumour. None of the common therapies is satisfactory. This study compared the efficacy of 30% hydrogen peroxide versus 50% trichloroacetic acid in treatment of seborrheic keratoses. METHODS This unblinded, comparative interventional study was conducted on 85 patients of seborrheic keratosis. Lesions were graded using physician's lesion assessment (PLA) scale (0, clear; 1, nearly clear; 2≤1 mm thick; 3≥1 mm thick). Patients were randomly assigned into two groups: A and B. Five target seborrheic keratosis lesions with PLA score of three were selected in each patient. Group A was treated with 30% hydrogen peroxide and group B with 50% trichloroacetic acid. Both groups received treatment every week up to 5 weeks or till complete resolution of lesions. Clinical photographs of all patients were taken every 2 weeks. RESULTS Both the groups were comparable in terms of age and sex. At the end of 6 weeks, lesions were cleared completely in 41.8% of patients in group A and in 23.8% in group B. Mean PLA score decreased significantly in group A from 3 to 1.79 (40.3%) at 2 weeks and to 0.63 (79%) at 6 weeks (P

Aim To perform a systematic literature review and network meta-analysis (NMA) of randomized controlled trials (RCTs) to estimate effect of SGLT2 inhibitors on fracture risk in patients with T2DM. Methods A systematic search was performed on PubMed/Medline and ClinicalTrials.gov from inception to May 2018 to identify RCTs reporting fracture events with the use of SGLT2 inhibitors compared to control group in patients with T2DM. NMA within a Bayesian framework was performed to calculate the odds ratio (OR) and 95% credible intervals (CrI) using random effect model. Node splitting method was applied to evaluate the presence of inconsistency for NMA. Results A total of 40 RCTs including 32,343 T2DM patients with 466 fracture cases. Pairwise meta-analysis showed no association between risk of fracture and SGLT2 inhibitors use (OR = 1.01, 95%CI 0.83–1.23; p = 0.91; I2 = 27%) compared with the control group. The NMA has shown a non-significant association with fracture risk and canagliflozin (OR = 0.57, 95%CrI 0.12–1.90), dapagliflozin (OR = 0.58, 95%CrI 0.13–2.00), and empagliflozin (OR = 0.78, 95%CrI 0.23–2.80) use when compared to placebo. No association was also found among SGLT-2 inhibitors (canagliflozin OR = 2.6, 95%CrI 0.69–16.00; dapagliflozin OR = 2.6, 95%CrI 0.52–22.00; and empagliflozin OR = 3.7, 95% CrI 1.0–27.00), when compared to active treatment. Node-splitting analysis shows non-significant inconsistency between direct and indirect comparisons. Conclusion The current NMA result suggests no detrimental effect of SGLT2 inhibitors on fracture risk in patients with T2DM. Further RCTs and real-world studies with long-term follow up are required to confirm the present findings.

A novel conjugated cationic polyfluorene (polyelectrolyte) derivative, PFBT, was developed by means of simple and cost-effective oxidative coupling polymerization method. PFBT displayed dual state emission in dimethyl sulfoxide (DMSO) as well as in water, a characteristic phenomenon of polyfluorene homopolymers, and tested for nitroexplosive analytes detection to observe a remarkable fluorescence quenching response for picric acid (PA) in the both solvents. The polymer PFBT demonstrated substantial selectivity and ultrasensitivity toward nitroexplosive PA in both the solvents (DMSO and H2O) with exceptional quenching constant values of 2.69 × 104 and 2.18 × 105 M-1 and a ultralow limit of detection of 92.7 nM (21.23 ppb) and 0.19 nM (43.53 ppt) in respective solvents. Furthermore, economical portable test strip devices were prepared for easy and fast on-site PA sensing, which can detect up to 0.22 ag level of PA. PA sensing in vapor phase was also established, that could detect up to 42.6 ppb level of PA vapors. Interestingly, the mechanism of sensing in DMSO solvent was attributed to substantial inner filter effect and photoinduced electron transfer, while in H2O the sensing occurs via possible resonance energy transfer and photoinduced electron transfer, which is exceptional and not reported earlier for a single probe.