Deep neural network model for enhancing disease prediction using auto encoder based broad learning.

Bioinformatics and Healthcare Integration Disease prediction models have been revolutionized by Big Data. These models, which make use of extensive medical data, predict illnesses before symptoms appear. Deep neural networks are well-known for their ability to increase accuracy by extending the network's depth and modifying weights through gradient descent. Traditional approaches, however, are hindered by issues such as gradient instability and delayed training. As a substitute, the Broad Learning (BL) system is introduced, which avoids gradient descent in favor of quick reconstruction by incremental learning. However, BL has trouble extracting complicated features from medical data, which makes it perform poorly in cases involving complex healthcare. We suggest ABL, which combines the effectiveness of BL with the noise reduction of Denoising Auto Encoder (AE), to address this. Robust feature extraction is an area in which the hybrid model shines, especially in intricate medical environments. Accuracy of up to 98.50 % is achieved by remarkable results from validation using a variety of datasets. The ability of ABL to quickly adapt through incremental learning suggests that it may be used to forecast diseases in complicated healthcare contexts with agility and accuracy.

2D Hybrid Nanocomposite Materials (h-BN/G/MoS2) as a High-Performance Supercapacitor Electrode.

The nanocomposites of hexagonal boron nitride, molybdenum disulfide, and graphene (h-BN/G/MoS2) are promising energy storage materials. The originality of the current work is the first-ever synthesis of 2D-layered ternary nanocomposites of boron nitrate, graphene, and molybdenum disulfide (h-BN/G/MoS2) using ball milling and the sonication method and the investigation of their applicability for supercapacitor applications. The morphological investigation confirms the well-dispersed composite material production, and the ternary composite appears to be made of h-BN and MoS2 wrapping graphene. The electrochemical characterization of the prepared samples is evaluated by cyclic voltammetry and galvanostatic charge/discharge tests. With a high specific capacitance of 392 F g-1 at a current density of 1 A g-1 and an outstanding cycling stability with around 96.4% capacitance retention after 10,000 cycles, the ideal 5% BN_G@MoS2_90@10 composite demonstrates exceptional capabilities. Furthermore, a symmetric supercapacitor (5% BN_G@MoS2_90@10 composite) exhibits a 94.1% capacitance retention rate even after 10,000 cycles, an energy density of 16.4 W h kg-1, and a power density of 501 W kg-1. The findings show that the preparation procedure is safe for the environment, manageable, and suitable for mass production, which is crucial for advancing the electrode materials used in supercapacitors.

Pancreaticoureteric fistula following penetrating abdominal trauma: A Case Report.

Pancreaticoureteric Fistula (PUF) is a very rare complication secondary to penetrating abdominal trauma involving the ureter and pancreatic parenchyma. Pancreatic injuries carry h igh morbidity due to the involvem ent of surrounding structures and are d ifficult to diagnose due to thei r retroperitoneal location. A case of a patient is reported at Civil Hospital, Hyderabad who presented with a history of firearm injury and missed pancreatic duct involvement on initial exploration that eventually led to the development of Pan creaticoureteric Fistula. He was managed v ia p erc ut aneous nep hrostomy ( PCN ) for the right ureteric injury and pancreatic duct (PD) stenting was done for distal main pancreatic duct injury (MPD).

Blackberry Seeds-Derived Carbon as Stable Anodes for Lithium-Ion Batteries.

The suitability of biocarbons derived from blackberry seeds as anode materials in lithium-ion batteries has been assessed for the first time. Blackberry seeds have antibacterial, anticancer, antidysentery, antidiabetic, antidiarrheal, and potent antioxidant properties and are generally used for herbal medical purposes. Carbon is extracted from blackberries using a straightforward carbonization technique and activated with KOH at temperatures 700, 800, and 900 °C. The physical characterization demonstrates that activated blackberry seeds-derived carbon at 900 °C (ABBSC-900 °C) have well-ordered graphene sheets with high defects compared to the ABBSC-700 °C and ABBSC-800 °C. It is discovered that an ABBSC-900 °C is mesoporous, with a notable Brunauer-Emmett-Teller surface area of 65 m2 g-1. ABBSC-900 has good electrochemical characteristics, as studied under 100 and 1000 mA g-1 discharge conditions when used as a lithium intercalating anode. Delivered against a 500 mA g-1 current density, a steady reversible capacity of 482 mA h g-1 has been achieved even after 200 cycles. It is thought that disordered mesoporous carbon with a large surface area account for the improved electrochemical characteristics of the ABBSC-900 anode compared to the other ABBSC-700 and ABBSC-800 carbons. The research shows how to use a waste product, ABBSC, as the most desired anode for energy storage applications.

Substitution of fishmeal: Highlights of potential plant protein sources for aquaculture sustainability.

High protein content, excellent amino acid profile, absence of anti-nutritional factors (ANFs), high digestibility and good palatability of fishmeal (FM), make it a major source of protein in aquaculture. Naturally derived FM is at risk due to an increase in its demand, unsustainable practices, and price. Thus, there is an urgent need to find affordable and suitable protein sources to replace FM. Plant protein sources are suitable due to their widespread availability and low cost. However, they contained certain ANFs, deficiency of some amino acids, low nutrient bioavailability and poor digestibility due to presence of starch and fiber. These unfavourable characteristics make them less suitable for feed as compared to FM. Thus, these potential challenges and limitations associated with various plant proteins have to be overcome by using different methods, i.e. enzymatic pretreatments, solvent extraction, heat treatments and fermentation, that are discussed briefly in this review. This review assessed the impacts of plant products on growth performance, body composition, flesh quality, changes in metabolic activities and immune response of fishes. To minimize the negative effects and to enhance nutritional value of plant products, beneficial functional additives such as citric acid, phytase and probiotics could be incorporated into the plant-based FM. Interestingly, these additives improve growth of fishes by increasing digestibility and nutrient utilization of plant based feeds. Overall, this review demonstrated that the substitution of fishmeal by plant protein sources is a plausible, technically-viable and practical option for sustainable aquaculture feed production.

Flexible and Freestanding MoS2/Graphene Composite for High-Performance Supercapacitors.

Two-dimensional atomically thick materials such as graphene and layered molybdenum disulfide (MoS2) have been studied as potential energy storage materials because of their high specific surface area, potential redox activity, and mechanical flexibility. However, because of the layered structure restacking and poor electrical conductivity, these materials are unable to attain their full potential. Composite electrodes made of a mixture of graphene and MoS2 have been shown to partially resolve these issues in the past, although their performance is still limited by inadequate mixing at the nanoscale. Herein, we report three composites via a simple ball-milling method and analyze supercapacitor electrodes. Compared with pristine graphene and MoS2, the composites showed high capacitance. The as-obtained MoS2@Graphene composite (1:9) possesses a high surface area and uniform dispersion of MoS2 on the graphene sheet. The MoS2@Graphene (1:9) composite electrode has a high specific capacitance of 248 F g-1 at 5 A g-1 in an electrochemical supercapacitor compared with the other two composites. Simultaneously, the flexible symmetric supercapacitor device prepared demonstrated superior flexibility and a long lifespan (93% capacitance retention after 8000 cycles) with no obvious changes in performance under different angles. In portable and wearable energy storage devices, the current experimental results will result in scalable, freestanding hybrid electrodes with improved, flexible, supercapacitive performance.

Diabetes Self-Care Activities and Their Relationship With Glycemic Control in Patients Visiting Hayatabad Medical Complex, Peshawar.

Background A significant portion of the Pakistani population is affected by diabetes, which has emerged as a global healthcare concern. Objective This study aimed to assess the correlation between glycemic control in diabetes patients and their engagement in diabetes self-care activities (DSCA). Methodology Cross-sectional research was conducted at Hayatabad Medical Complex in Peshawar between June 2019 and May 2020. A total of 280 carefully selected patients with type 2 diabetes mellitus (T2DM) were included. Data collection involved an interviewer-administered questionnaire encompassing sociodemographic information, diabetes-related data, and the summary of the Diabetes Self-Care Activities (SDSCA) scale. Descriptive statistics and Pearson's chi-square test were employed for data analysis. Results The study observed that the majority of participants (40.36%) were females, and the age range of the participants was between 42 and 53 years (68.22%). According to the study, 55.00% of participants had a normal body mass index (BMI), and 71.08% of participants had diabetes in their family. Regarding glycemic control, 55.71% of individuals exhibited good control based on fasting blood sugar (FBS) levels while 74.64% showed poor control according to hemoglobin A1C (HbA1c) values. HbA1c was substantially linked with a general diet (healthy eating plan), physical activities, and adherence to medication ((odds ratios (OR): 3.12), (95% confidence interval (CI): 1.02 - 8.78), (P value: 0.031)); ((OR: 2.19, 95%), (CI:1.18 - 3.79), (P value: 0.003)); ((OR: 2.85), (95% CI: 1.22 - 6.59), P value: 0.021)). Conclusion The findings indicated that health professionals need to create health education programs on DSCA in order to increase DSCA adherence in people with T2DM while maintaining glycemic control.

A Pressure-Based Multiphase Flowmeter: Proof of Concept.

Multiphase flowmeters (MPFMs) measure the flow rates of oil, gas, and brine in a pipeline. MPFMs provide remote access to real-time well production data that are essential for efficient oil field operations. Most MPFMs are complex systems requiring frequent maintenance. An MPFM that is operationally simple and accurate is highly sought after in the energy industry. This paper describes an MPFM that uses only pressure sensors to measure gas and liquid flow rates. The design is an integration of a previously developed densitometer with an innovative Venturi-type flowmeter. New computing models with strong analytical foundations were developed, aided by empirical correlations and machine-learning-based flow-regime identification. A prototype was experimentally validated in a multiphase flow loop over a wide range of field-like conditions. The accuracy of the MPFM was compared to that of other multiphase metering techniques from similar studies. The results point to a robust, practical MPFM.

Liver Transplantation: A Right or a Privilege? Sustainable Liver Transplant Financing With an Innovative Model for the Developing World.

Living donor liver transplant in addition to its lifesaving therapy is a cost-effective alternate to long-term disease management in patients with chronic liver disease. Financial constraint is the biggest hurdle faced by patients in developing countries in availing the liver transplantation. So, we conducted this study to report a government-funded financial support system for liver transplant services. A total of 198 patients who underwent living donor liver transplant with at least 90 days follow-up were included in the study. According to proxy means test score, 52.2% patients were from low and middle socioeconomic groups and 64.6% of patients underwent liver transplantation through government support. Out of 198 patients who underwent liver transplantation 29.6% had monthly income below 25,000 Pakistani rupees ($114). In recipients, 90-day mortality was 7.1% and morbidity was 67.1%. Donor morbidity was 23.2% without any mortality. This financial model can serve as a valuable source for middle and low income group countries to overcome the financial challenge and make liver transplant an accessible, affordable, and economically viable option.

Launching Liver Exchange and the First 3-Way Liver Paired Donation.

This article discusses the successful implementation of a liver exchange mechanism that led to 3 liver allotransplants and 3 hepatectomies between 3 incompatible patient-donor pairs.

Prompt Deep Light-Weight Vessel Segmentation Network (PLVS-Net).

Achieving accurate retinal vessel segmentation is critical in the progression and diagnosis of vision-threatening diseases such as diabetic retinopathy and age-related macular degeneration. Existing vessel segmentation methods are based on encoder-decoder architectures, which frequently fail to take into account the retinal vessel structure's context in their analysis. As a result, such methods have difficulty bridging the semantic gap between encoder and decoder characteristics. This paper proposes a Prompt Deep Light-weight Vessel Segmentation Network (PLVS-Net) to address these issues by using prompt blocks. Each prompt block use combination of asymmetric kernel convolutions, depth-wise separable convolutions, and ordinary convolutions to extract useful features. This novel strategy improves the performance of the segmentation network while simultaneously decreasing the number of trainable parameters. Our method outperformed competing approaches in the literature on three benchmark datasets, including DRIVE, STARE, and CHASE.

Sensor Data Fusion Based on Deep Learning for Computer Vision Applications and Medical Applications.

Sensor fusion is the process of merging data from many sources, such as radar, lidar and camera sensors, to provide less uncertain information compared to the information collected from single source [...].

Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network for Embryonic Analysis.

Infertility is one of the most important health concerns worldwide. It is characterized by not being successful of pregnancy after some periods of periodic unprotected sexual intercourse. In vitro fertilization (IVF) is an assisted reproduction technique that efficiently addresses infertility. IVF replaces the actual mode of reproduction through a manual procedure wherein embryos are cultivated in a controlled laboratory environment until they reach the blastocyst stage. The standard IVF procedure includes the transfer of one or two blastocysts from several blastocysts that are grown in a controlled environment. The morphometric properties of blastocysts with their compartments such as trophectoderm (TE), zona pellucida (ZP), inner cell mass (ICM), and blastocoel (BL), are analyzed through manual microscopic analysis to predict viability. Deep learning has been extensively used for medical diagnosis and analysis and can be a powerful tool to automate the morphological analysis of human blastocysts. However, the existing approaches are inaccurate and require extensive preprocessing and expensive architectures. Thus, to cope with the automatic detection of blastocyst components, this study proposed a novel multiscale aggregation semantic segmentation network (MASS-Net) that combined four different scales via depth-wise concatenation. The extensive use of depthwise separable convolutions resulted in a decrease in the number of trainable parameters. Further, the innovative multiscale design provided rich spatial information of different resolutions, thereby achieving good segmentation performance without a very deep architecture. MASS-Net utilized 2.06 million trainable parameters and accurately detects TE, ZP, ICM, and BL without using preprocessing stages. Moreover, it can provide a separate binary mask for each blastocyst component simultaneously, and these masks provide the structure of each component for embryonic analysis. Further, the proposed MASS-Net was evaluated using publicly available human blastocyst (microscopic) imaging data. The experimental results revealed that it can effectively detect TE, ZP, ICM, and BL with mean Jaccard indices of 79.08, 84.69, 85.88%, and 89.28%, respectively, for embryological analysis, which was higher than those of the state-of-the-art methods.

Novel Synthesis of Sensitive Cu-ZnO Nanorod-Based Sensor for Hydrogen Peroxide Sensing.

We aimed to synthesize sensitive electrochemical sensors for hydrogen peroxide sensing by using zinc oxide nanorods grown on a fluorine-doped tin oxide electrode by using the facial hydrothermal method. It was essential to keep the surface morphology of the material (nanorods structure); due to its large surface area, the concerned material has enhanced detection ability toward the analyte. The work presents a non-enzymatic H2O2 sensor using vertically grown zinc oxide nanorods on the electrode (FTO) surfaces with Cu nanoparticles deposited on zinc oxide nanorods to enhance the activity. Scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), energy-dispersive X-Ray (EDX), X-ray diffraction (XRD), and electrochemical methods were used to characterize copper-zinc oxide nanorods. In addition to the high surface area, the hexagonal Cu-ZnO nanorods exhibited enhanced electrochemical features of H2O2 oxidation. Nanorods made from Cu-ZnO exhibit highly efficient sensitivity of 3415 µAmM-1cm-2 low detection limits (LODs) of 0.16 µM and extremely wide linear ranges (0.001-11 mM). In addition, copper-zinc oxide nanorods demonstrated decent reproducibility, repeatability, stability, and selectivity after being used for H2O2 sensing in water samples with an RSD value of 3.83%. Cu nanoparticles decorated on ZnO nanorods demonstrate excellent potential for the detection of hydrogen peroxide, providing a new way to prepare hydrogen peroxide detecting devices.

Early-onset aggressive oesophageal carcinoma in Down's syndrome - a coincidence or a game of extra genes?

Patients with Down's syndrome exhibit a unique pattern for a number of malignant conditions but there is inconsistent data for the risk of oesophageal cancer. We present a case of early-onset aggressive oesophageal carcinoma in a young male patient diagnosed with Trisomy 21, who presented with complaints of progressive dysphagia, vomiting, voice change and weight loss. Barium swallow showed shouldering sign at distal oesophagus. GI Endoscopy revealed an irregular growth at 20cm from incisors obstructing the lumen. Histopathology confirmed well-differentiated adenocarcinoma. CT scan unmasked a circumferential mass involving the dorsal oesophagus with multiple enlarged nodes along with infiltration of basal segments of left lung staging the tumour as T3N1M0. A metallic stent was placed endoscopically through the stenotic tumour and the patient was referred for chemoradiotherapy. Contrary to the literature proposing a decreased incidence of solid tumours, this is a case reporting early-onset aggressive oesophageal carcinoma in a patient with Down's syndrome.

Deep Features Aggregation-Based Joint Segmentation of Cytoplasm and Nuclei in White Blood Cells.

White blood cells (WBCs), also known as leukocytes, are one of the valuable parts of the blood and immune system. Typically, pathologists use microscope for the manual inspection of blood smears which is a time-consuming, error-prone, and labor-intensive procedure. To address these issues, we present two novel shallow networks: a leukocyte deep segmentation network (LDS-Net) and leukocyte deep aggregation segmentation network (LDAS-Net) for the joint segmentation of cytoplasm and nuclei in WBC images. LDS-Net is a shallow architecture with three downsampling stages and seven convolution layers. LDAS-Net is an extended version of LDS-Net that utilizes a novel pool-less low-level information transfer bridge to transfer low-level information to the deep layers of the network. This information is aggregated with deep features in a dense feature concatenation block to achieve accurate cytoplasm and nuclei joint segmentation. We evaluated our developed architectures on four WBC publicly available datasets. For cytoplasmic segmentation in WBCs, the proposed method achieved the dice coefficients of 98.97%, 99.0%, 96.05%, and 98.79% on Datasets 1, 2, 3, and 4, respectively. For nuclei segmentation, the dice coefficients of 96.35% and 98.09% are achieved for Datasets 1 and 2, respectively. Proposed method outperforms state-of-the-art methods with superior computational efficiency and requires only 6.5 million trainable parameters.

Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges.

Frequency-modulated continuous wave radar sensors play an essential role for assisted and autonomous driving as they are robust under all weather and light conditions. However, the rising number of transmitters and receivers for obtaining a higher angular resolution increases the cost for digital signal processing. One promising approach for energy-efficient signal processing is the usage of brain-inspired spiking neural networks (SNNs) implemented on neuromorphic hardware. In this article we perform a step-by-step analysis of automotive radar processing and argue how spiking neural networks could replace or complement the conventional processing. We provide SNN examples for two processing steps and evaluate their accuracy and computational efficiency. For radar target detection, an SNN with temporal coding is competitive to the conventional approach at a low compute overhead. Instead, our SNN for target classification achieves an accuracy close to a reference artificial neural network while requiring 200 times less operations. Finally, we discuss the specific requirements and challenges for SNN-based radar processing on neuromorphic hardware. This study proves the general applicability of SNNs for automotive radar processing and sustains the prospect of energy-efficient realizations in automated vehicles.

Detecting Blastocyst Components by Artificial Intelligence for Human Embryological Analysis to Improve Success Rate of In Vitro Fertilization.

Morphological attributes of human blastocyst components and their characteristics are highly correlated with the success rate of in vitro fertilization (IVF). Blastocyst component analysis aims to choose the most viable embryos to improve the success rate of IVF. The embryologist evaluates blastocyst viability by manual microscopic assessment of its components, such as zona pellucida (ZP), trophectoderm (TE), blastocoel (BL), and inner cell mass (ICM). With the success of deep learning in the medical diagnosis domain, semantic segmentation has the potential to detect crucial components of human blastocysts for computerized analysis. In this study, a sprint semantic segmentation network (SSS-Net) is proposed to accurately detect blastocyst components for embryological analysis. The proposed method is based on a fully convolutional semantic segmentation scheme that provides the pixel-wise classification of important blastocyst components that help to automatically check the morphologies of these elements. The proposed SSS-Net uses the sprint convolutional block (SCB), which uses asymmetric kernel convolutions in combination with depth-wise separable convolutions to reduce the overall cost of the network. SSS-Net is a shallow architecture with dense feature aggregation, which helps in better segmentation. The proposed SSS-Net consumes a smaller number of trainable parameters (4.04 million) compared to state-of-the-art methods. The SSS-Net was evaluated using a publicly available human blastocyst image dataset for component segmentation. The experimental results confirm that our proposal provides promising segmentation performance with a Jaccard Index of 82.88%, 77.40%, 88.39%, 84.94%, and 96.03% for ZP, TE, BL, ICM, and background, with residual connectivity, respectively. It is also provides a Jaccard Index of 84.51%, 78.15%, 88.68%, 84.50%, and 95.82% for ZP, TE, BL, ICM, and background, with dense connectivity, respectively. The proposed SSS-Net is providing a mean Jaccard Index (Mean JI) of 85.93% and 86.34% with residual and dense connectivity, respectively; this shows effective segmentation of blastocyst components for embryological analysis.

A review on gasification and pyrolysis of waste plastics.

Gasification and pyrolysis are thermal processes for converting carbonaceous substances into tar, ash, coke, char, and gas. Pyrolysis produces products such as char, tar, and gas, while gasification transforms carbon-containing products (e.g., the products from pyrolysis) into a primarily gaseous product. The composition of the products and their relative quantities are highly dependent on the configuration of the overall process and on the input fuel. Although in gasification, pyrolysis processes also occur in many cases (yet prior to the gasification processes), gasification is a common description for the overall technology. Pyrolysis, on the other hand, can be used without going through the gasification process. The current study evaluates the most common waste plastics valorization routes for producing gaseous and liquid products, as well as the key process specifications that affected the end final products. The reactor type, temperatures, residence time, pressure, the fluidizing gas type, the flow rate, and catalysts were all investigated in this study. Pyrolysis and waste gasification, on the other hand, are expected to become more common in the future. One explanation for this is that public opinion on the incineration of waste in some countries is a main impediment to the development of new incineration capacity. However, an exceptional capability of gasification and pyrolysis over incineration to conserve waste chemical energy is also essential.

Formulation and evaluation of an ointment from Pinus gerardiana extracts indigenous to Balochistan.

In skin disorders such as microbial and fungal infections, plants and their parts are used. However, there have been very few scientific reports of herbal extracts of the plant Pinus gerardiana to be administered transdermally. The antifungal activity was assessed using poisoned food method against the strains of three pathogenic fungi, namely Alternaria alternata, Curvularia lunata and Bipolaris specifera. Ointment was prepared according to British pharmacopeia and physiochemical evaluation tests were performed. The GCMS was used to determine the chemical composition of the essential oil of Pinus gerardiana. 27 components were obtained. Monoterpenes= 89.97%, Oxygenated monoterpenes = 8.75%, Sesquiterpenes = 2.21% out of 100% of the total composition. The extract of pinus gerardiana showed a zone of inhibition on organism Bipolaris specifera 2.98±0.1µg/ml, Alternaria alternate 3.48±0.21µ/ml and Curvularia lunata 5.04±0.24µg/ml. Ointment was prepared with pH 5.9, conductivity 0.1, viscosity 22.24 and tested for stability. Franz cells were used in vitro and release was determined from 30 minutes to 12 hours.