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).

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.

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 [...].

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.

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.

A hybrid of ultrathin metal-organic framework sheet and ultrasmall copper nanoparticles for detection of hydrogen peroxide with enhanced activity.

Here, we design and synthesize a novel 2D Cu-tetrakis(4-carboxyphenyl)porphyrin (TCPP) metal-organic framework (MOF) sheet and ultrasmall Cu5.4O nanoparticle (Cu5.4O USNP) hybrid (Cu-TCPP MOF/Cu5.4O nanocomposite). The graphene-like ultrathin Cu-TCPP MOF sheets offer high surface-to-volume atom ratios and many active sites, which is beneficial for loading more Cu5.4O USNPs. The Cu5.4O USNPs with ultrasmall size (<5 nm) have promising conductivity and excellent enzymatic ability for H2O2. The successfully prepared nanocomposites are characterized by transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and Fourier transform infrared (FT-IR) techniques. The 2D graphene-like ultrathin Cu-TCPP MOF sheets show no H2O2-sensing signals, whereas Cu5.4O USNPs exhibit a clear reduction peak for detection of H2O2. Interestingly, the combination of two kinds of nanomaterials improved the H2O2 sensing ability due to their synergistic effect. The properties of the unmodified electrodes and the Cu-TCPP MOF/Cu5.4O nanocomposite-modified electrodes were systemically studied by cyclic voltammetry (CV), current-time (i-t) response, and square-wave voltammetry (SWV) techniques. The electrochemical sensor for the detection of H2O2 based on the Cu-TCPP MOF/Cu5.4O nanocomposite has a lower detection limit of 0.13 µmol·L-1 and wider linear range of 0.1 × 10-6 ~ 0.59 × 10-3 mol·L-1 and 1.59 × 10-3 ~ 20.59 × 10-3 mol·L-1 when compared with the Cu5.4O USNPs-modified electrode. The electrochemical sensor can be further used to detect H2O2 produced by cells. Graphical abstract The mechanism for sensing H2O2 produced from cells based on a Cu-TCPP MOF/Cu5.4O USNPs nanocomposite-modified electrode.

Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning-Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation.

BACKGROUND: The early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning-based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract. OBJECTIVE: This study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases. METHODS: Our proposed framework comprises a deep learning-based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment. RESULTS: All the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods. CONCLUSIONS: This study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences.

Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa.

Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and hypertensive retinopathy. RP is a genetic retinal disorder that leads to progressive vision degeneration and initially causes night blindness. Currently, the most commonly applied method for diagnosing retinal diseases is optical coherence tomography (OCT)-based disease analysis. In contrast, fundus imaging-based disease diagnosis is considered a low-cost diagnostic solution for retinal diseases. This study focuses on the detection of RP from the fundus image, which is a crucial task because of the low quality of fundus images and non-cooperative image acquisition conditions. Automatic detection of pigment signs in fundus images can help ophthalmologists and medical practitioners in diagnosing and analyzing RP disorders. To accurately segment pigment signs for diagnostic purposes, we present an automatic RP segmentation network (RPS-Net), which is a specifically designed deep learning-based semantic segmentation network to accurately detect and segment the pigment signs with fewer trainable parameters. Compared with the conventional deep learning methods, the proposed method applies a feature enhancement policy through multiple dense connections between the convolutional layers, which enables the network to discriminate between normal and diseased eyes, and accurately segment the diseased area from the background. Because pigment spots can be very small and consist of very few pixels, the RPS-Net provides fine segmentation, even in the case of degraded images, by importing high-frequency information from the preceding layers through concatenation inside and outside the encoder-decoder. To evaluate the proposed RPS-Net, experiments were performed based on 4-fold cross-validation using the publicly available Retinal Images for Pigment Signs (RIPS) dataset for detection and segmentation of retinal pigments. Experimental results show that RPS-Net achieved superior segmentation performance for RP diagnosis, compared with the state-of-the-art methods.

Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models.

In vivo diseases such as colorectal cancer and gastric cancer are increasingly occurring in humans. These are two of the most common types of cancer that cause death worldwide. Therefore, the early detection and treatment of these types of cancer are crucial for saving lives. With the advances in technology and image processing techniques, computer-aided diagnosis (CAD) systems have been developed and applied in several medical systems to assist doctors in diagnosing diseases using imaging technology. In this study, we propose a CAD method to preclassify the in vivo endoscopic images into negative (images without evidence of a disease) and positive (images that possibly include pathological sites such as a polyp or suspected regions including complex vascular information) cases. The goal of our study is to assist doctors to focus on the positive frames of endoscopic sequence rather than the negative frames. Consequently, we can help in enhancing the performance and mitigating the efforts of doctors in the diagnosis procedure. Although previous studies were conducted to solve this problem, they were mostly based on a single classification model, thus limiting the classification performance. Thus, we propose the use of multiple classification models based on ensemble learning techniques to enhance the performance of pathological site classification. Through experiments with an open database, we confirmed that the ensemble of multiple deep learning-based models with different network architectures is more efficient for enhancing the performance of pathological site classification using a CAD system as compared to the state-of-the-art methods.

Normalized differential method for improving the signal-to-noise ratio of a distributed acoustic sensor.

We experimentally introduce a normalized differential method to enhance the time domain signal-to-noise ratio (SNR) of an optical fiber distributed acoustic sensor (DAS). The reported method is calibrated against the typical differential method in noisy DAS systems, including those utilizing a relatively wide linewidth laser or few-mode fiber. In these two systems, the normalized differential method respectively identifies the position information of various vibration events with 1.7 dB and 0.53 dB SNR improvement. We further demonstrate the ability to locate positions along a fiber that are subjected to vibrations of frequencies higher than the theoretical maximum, but without determining these frequencies.

Deep Residual CNN-Based Ocular Recognition Based on Rough Pupil Detection in the Images by NIR Camera Sensor.

Accurate segmentation of the iris area in input images has a significant effect on the accuracy of iris recognition and is a very important preprocessing step in the overall iris recognition process. In previous studies on iris recognition, however, the accuracy of iris segmentation was reduced when the images of captured irises were of low quality due to problems such as optical and motion blurring, thick eyelashes, and light reflected from eyeglasses. Deep learning-based iris segmentation has been proposed to improve accuracy, but its disadvantage is that it requires a long processing time. To resolve this problem, this study proposes a new method that quickly finds a rough iris box area without accurately segmenting the iris region in the input images and performs ocular recognition based on this. To address this problem of reduced accuracy, the recognition is performed using the ocular area, which is a little larger than the iris area, and a deep residual network (ResNet) is used to resolve the problem of reduced recognition rates due to misalignment between the enrolled and recognition iris images. Experiments were performed using three databases: Institute of Automation Chinese Academy of Sciences (CASIA)-Iris-Distance, CASIA-Iris-Lamp, and CASIA-Iris-Thousand. They confirmed that the method proposed in this study had a higher recognition accuracy than existing methods.

Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor.

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works.

Deep Learning-Based Gaze Detection System for Automobile Drivers Using a NIR Camera Sensor.

A paradigm shift is required to prevent the increasing automobile accident deaths that are mostly due to the inattentive behavior of drivers. Knowledge of gaze region can provide valuable information regarding a driver's point of attention. Accurate and inexpensive gaze classification systems in cars can improve safe driving. However, monitoring real-time driving behaviors and conditions presents some challenges: dizziness due to long drives, extreme lighting variations, glasses reflections, and occlusions. Past studies on gaze detection in cars have been chiefly based on head movements. The margin of error in gaze detection increases when drivers gaze at objects by moving their eyes without moving their heads. To solve this problem, a pupil center corneal reflection (PCCR)-based method has been considered. However, the error of accurately detecting the pupil center and corneal reflection center is increased in a car environment due to various environment light changes, reflections on glasses surface, and motion and optical blurring of captured eye image. In addition, existing PCCR-based methods require initial user calibration, which is difficult to perform in a car environment. To address this issue, we propose a deep learning-based gaze detection method using a near-infrared (NIR) camera sensor considering driver head and eye movement that does not require any initial user calibration. The proposed system is evaluated on our self-constructed database as well as on open Columbia gaze dataset (CAVE-DB). The proposed method demonstrated greater accuracy than the previous gaze classification methods.

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors.

The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets.

LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV Landing by Visible Light Camera Sensor on Drone.

Autonomous landing of an unmanned aerial vehicle or a drone is a challenging problem for the robotics research community. Previous researchers have attempted to solve this problem by combining multiple sensors such as global positioning system (GPS) receivers, inertial measurement unit, and multiple camera systems. Although these approaches successfully estimate an unmanned aerial vehicle location during landing, many calibration processes are required to achieve good detection accuracy. In addition, cases where drones operate in heterogeneous areas with no GPS signal should be considered. To overcome these problems, we determined how to safely land a drone in a GPS-denied environment using our remote-marker-based tracking algorithm based on a single visible-light-camera sensor. Instead of using hand-crafted features, our algorithm includes a convolutional neural network named lightDenseYOLO to extract trained features from an input image to predict a marker's location by visible light camera sensor on drone. Experimental results show that our method significantly outperforms state-of-the-art object trackers both using and not using convolutional neural network in terms of both accuracy and processing time.

Fuzzy System-Based Target Selection for a NIR Camera-Based Gaze Tracker.

Gaze-based interaction (GBI) techniques have been a popular subject of research in the last few decades. Among other applications, GBI can be used by persons with disabilities to perform everyday tasks, as a game interface, and can play a pivotal role in the human computer interface (HCI) field. While gaze tracking systems have shown high accuracy in GBI, detecting a user's gaze for target selection is a challenging problem that needs to be considered while using a gaze detection system. Past research has used the blinking of the eyes for this purpose as well as dwell time-based methods, but these techniques are either inconvenient for the user or requires a long time for target selection. Therefore, in this paper, we propose a method for fuzzy system-based target selection for near-infrared (NIR) camera-based gaze trackers. The results of experiments performed in addition to tests of the usability and on-screen keyboard use of the proposed method show that it is better than previous methods.

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.

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.

A 2D hybrid nanocomposite: a promising anode material for lithium-ion batteries at high temperature.

Two-dimensional atomically thick materials including graphene, BN, and molybdenum disulfide (MoS2) have been investigated as possible energy storage materials, because of their large specific surface area, potential redox activity, and mechanical stability. Unfortunately, these materials cannot reach their full potential due to their low electrical conductivity and layered structural restacking. These problems have been somewhat resolved in the past by composite electrodes composed of a graphene and MoS2 mixture; however, insufficient mixing at the nanoscale still limits performance. Here, we examined lithium-ion battery electrodes and reported three composites made using a basic ball milling technique and sonication method. The 5% BN-G@MoS2-50@50 composite obtained has a homogeneous distribution of MoS2 on the graphene sheet and H-BN with high crystallinity. Compared to the other two composites (5% BN-G@MoS2-10@90 and 5% BN-G@MoS2-90@10), the 5% BN-G@MoS2-50@50 composite electrode exhibits a high specific capacity of 765 mA h g-1 and a current density of 100 mA g-1 in batteries. Additionally, the 5% BN-G@MoS2-50@50 composite electrode displays an excellent rate capability (453 mA h g-1 at a current density of 1000 mA g-1) at a high temperature of 70 °C, thanks to h-BN that allows reliable and safe operation of lithium-ion batteries. Our research may pave the way for the sensible design of different anode materials, including 2D materials (5% BN-G@MoS2-50@50) for high-performance LIBs and other energy-related fields.

Efficacy of Platelet-Rich Plasma in the Treatment of Diabetic Foot Ulcer.

Introduction Diabetic foot complications leading to limb amputations pose a global health concern. Platelet-rich plasma (PRP) gel has emerged as a promising method for ulcer healing, leveraging the growth factors provided by autologous PRP to enhance tissue healing. Therefore, we aimed to assess the frequency of the success of PRP therapy in the treatment of non-healing diabetic foot ulcers. Methods This quasi-experimental study, conducted in Lahore, Pakistan, from April 2021 to October 2022, enrolled 80 eligible individuals with non-responsive diabetic foot ulcers using a consecutive sampling technique. Inclusion criteria involved patients of both genders, aged 45-75 years, with unhealed diabetic foot ulcers, and exclusion criteria considered factors such as recurrent ulcers at the same site, smoking, and immunosuppressive or anticoagulant drug therapy. Baseline demographic details, ulcer measurements using a scale, and AutoCAD (Autodesk, Inc., San Francisco, California, United States)-assisted quantification of ulcer base were recorded. Autologous PRP injections were administered following strict aseptic protocols, with dressing changes and assessments performed at specified intervals over four weeks. Treatment success, defined as >90% healing after four weeks, was the primary outcome. Data analysis utilized IBM SPSS Statistics for Windows, Version 26.0 (Released 2019; IBM Corp., Armonk, New York, United States), employing post-stratification chi-square and t-tests where appropriate for significant differences. Results The mean age of the patients was 60.40 ± 9.72 years, the mean duration of diabetes was 9.48 ± 2.21 years, and the mean ulcer duration was 11.41 ± 1.63 months. The treatment success rate was 63.7%. Age, gender, and disease duration showed no significant impact on treatment success. However, patients with a normal BMI and shorter ulcer duration exhibited a significantly higher success rate (p <0.001 and p = 0.002, respectively). Conclusions This study reaffirms the efficacy of PRP in treating non-healing diabetic foot ulcers, aligning with previous research. Despite a slightly lower success rate compared to literature reports, PRP remains a promising agent for managing diabetic foot ulcers.