Microplastic Quantification in Aquatic Birds: Biomonitoring the Environmental Health of the Panjkora River Freshwater Ecosystem in Pakistan.

Microplastic pollution has become a global concern, with potential negative impacts on various ecosystems and wildlife species. Among these species, ducks (Anas platyrhynchos) are particularly vulnerable due to their feeding habits and proximity to aquatic environments contaminated with microplastics. The current study was designed to monitor microplastic (MP) pollutants in the freshwater ecosystem of the Panjkora River, Lower Dir, Pakistan. A total of twenty (20) duck samples were brought up for four months and 13 days on the banks of the river, with no food intake outside the river. When they reached an average weight of 2.41 ± 0.53 kg, all samples were sacrificed, dissected, and transported in an ice box to the laboratory for further analysis. After sample preparation, such as digestion with 10% potassium hydroxide (KOH), density separation, filtration, and identification, the MP content was counted. A total of 2033 MP particles were recovered from 20 ducks with a mean value of 44.6 ± 15.8 MPs/crop and 57.05 ± 18.7 MPs/gizzard. MPs detected in surface water were 31.2 ± 15.5 MPs/L. The major shape types of MPs recovered were fragments in crop (67%) and gizzard (58%) samples and fibers in surface water (56%). Other types of particles recovered were fibers, sheets, and foams. The majority of these detected MP particles were in the size range of 300-500 µm (63%) in crops, and 50-150 µm (55%) in gizzards, while in water samples the most detected particles were in the range of 150-300 µm (61%). Chemical characterization by FTIR found six types of polymers. Low-density polyethylene (LDPE) had the greatest polymer detection rate (39.2%), followed by polyvinyl chloride (PVC) (28.3%), high-density polyethylene (HDPE) (22.7%), polystyrene (6.6%), co-polymerized polypropylene (2.5%), and polypropylene homopolymer (0.7%). This study investigated the presence of microplastics in the crops and gizzards of ducks, as well as in river surface water. The results revealed the significant and pervasive occurrence of microplastics in both the avian digestive systems and the surrounding water environment. These findings highlight the potential threat of microplastic pollution to wildlife and ecosystems, emphasizing the need for further research and effective mitigation strategies to address this pressing environmental concern.

First-Principles Investigation of Novel Alkali-Based Lead-Free Halide Perovskites for Advanced Optoelectronic Applications.

Lead-free halide perovskites are considered promising candidates as visible light absorbers with outstanding optoelectronic properties. In this work, novel kinds of lead-free halide perovskites were studied for their electronic, optical, and thermoelectric properties by employing the most precise and enhanced modified Trans-Blaha Beck-Johnson potential. The estimated band spectra of the studied materials were comparable. The materials are confirmed to have an indirect band gap semiconducting nature due to the existence of energy band gaps. Among the studied materials, CsSnI3 has a smaller band gap, confirming the excitation to be more energy efficient. Examining the predicted density of states and true electronic orbital contributions, we observed a progressive fluctuation along the energy axis was observed. Furthermore, the linear optical properties are calculated and studied in terms of possible optoelectronic applications. The absorption in KSnI3 was greater compared to the other two materials. The studied materials could be used for antireflecting coatings against UV radiation, owing to the prominent peaks in their reflectivity spectra. The Seebeck coefficient and electrical properties, as well as the positive value of RH all pointed to a p-type nature in these materials. From the anticipated thermoelectric properties, the materials also appear to be suitable for application in thermoelectric devices.

Investigating the Optoelectronic and Thermoelectric Properties of CdTe Systems in Different Phases: A First-Principles Study.

CdTe is a potential material for making efficient and stable solar cells. The present study aimed to systematically investigate the electronic, optical, and thermoelectric properties of different structural phases of CdTe using density functional theory. The electronic properties were calculated using the modified Becke-Johnson potential with the local density approximation (LDA) correlation. The band structure profiles showed a direct band at the Γ-point for α-cubic, ß-hexagonal, γ-orthorhombic, and an indirect band type for the δ-trigonal phase from the A-point at valence band maximum to the Γ-point at conduction band minimum. Hybridization between Te-p and Cd-s bands in the main valence region was observed in the partial density of states plots for all the studied phases. The real component static values of the dielectric function showed a slight decrease with increasing photonic energy after an initial small increase. The intensity of the imaginary component increased above the threshold energy for each phase, with the δ-phase showing a higher reflectivity spectrum than the other phases due to its intense peaks, making it ideal for protecting against high energy radiations. The results indicated that our computed band gaps and refractive index n(ω) were inversely related. The thermoelectric parameters calculated for these phases suggest that they have potential to be used in thermoelectric devices.

Investigation of Structural, Mechanical, Optoelectronic, and Thermoelectric Properties of BaXF3 (X = Co, Ir) Fluoro-Perovskites: Promising Materials for Optoelectronic and Thermoelectric Applications.

Coded within Wien2K, we carry out DFT-based calculations for investigations of the structural, elastic, optoelectronic, and thermoelectric properties of BaXF3 (X = Co, Ir) fluoro-perovskites. The Birch-Murnaghan fit to the energy-vs-volume data and formation energy shows that these fluoro-perovskites are structurally stable. The phonon calculation confirms the thermodynamic stability, while the relation between elastic constants such as C 11 - C 12 > 0, C 11 > 0, C 11 + 2C 12 > 0, and B > 0 validates the mechanical stability of the compounds. BaIrF3 exhibits a strong ability to endure compressive and shear stresses. BaCoF3 shows a weaker capacity of withstanding changes in volume, attributed to a lower bulk modulus. Demonstrating a higher G-modulus of rigidity than the BaIrF3, BaCoF3 demonstrates stronger resistance to change the shape and both compounds are found to be anisotropic and brittle. The determined band structure profiles reveal that both BaCoF3 and BaIrF3 demonstrate a metallic nature. In addition, the metallic nature of BaCoF3 and BaIrF3 is reinforced by the density-of-states (DOS) study, where Co and F atoms contribute significantly to the total DOS in the valence band in the case of BaCoF3, while that of BaIrF3 is predominated by the Ba and F atoms. The computed values of ε1(0) for BaCoF3 and BaIrF3 are approximately 30 and 19, respectively, which are in line with Penn's model. The researched materials are confirmed to be strong contenders for optoelectronics by the lack of absorption in the visible range. For their potential use in thermoelectric device applications, thermoelectric parameters such as temperature-dependent Seebeck coefficient, specific heat capacity, thermal conductivity, power factor, and figure of merit are also investigated, which show that these materials are thermally stable and promising for applications in thermoelectric devices.

Computational evaluation of novel barium zinc chalcogenides Ba2ZnCh3 (Ch = S, Se, Te) for advanced optoelectronic applications.

Due to their outstanding optoelectronic characteristics, ternary chalcogenides are considered an attractive choice for absorbers of visible light. Here, a novel family of ternary chalcogenides, Ba2ZnCh3 (Ch = S, Se, Te), is examined in the context of density functional theory calculations. The atomic locations in these materials are determined before the bulk modulus, cohesive energy, and lattice constant predictions. Our examined materials are semiconductors by nature, as indicated by the electronic band profiles with both the WC-GGA potential and TB-mBJ potential revealing an energy gap between the valence and conduction bands. Using full geometrical optimization and the force minimization technique based on Broyden's scheme, the interior atomic positions and the relaxed structures were obtained. The formation energies are likely more consistent with a convex hull near the configuration of interest. The cohesive energies confirmed that Ba2ZnS3 has the most cohesive nature as compared to the other two materials. Likewise, the calculated components of the dielectric function of the individual materials were anisotropic having dissimilar values along different dimensions. The computed sharp peaks of the absorption coefficient for Ba2ZnS3, Ba2ZnSe3, and Ba2ZnTe3 confirm their improved absorption behavior in the UV range. Moreover, the thermoelectric characteristics were computed, and the findings are detailed, indicating that all of these materials are efficient for thermoelectric device applications.

PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms.

Cardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard.

Deep Learning Assisted Automated Assessment of Thalassaemia from Haemoglobin Electrophoresis Images.

Haemoglobin (Hb) electrophoresis is a method of blood testing used to detect thalassaemia. However, the interpretation of the result of the electrophoresis test itself is a complex task. Expert haematologists, specifically in developing countries, are relatively few in number and are usually overburdened. To assist them with their workload, in this paper we present a novel method for the automated assessment of thalassaemia using Hb electrophoresis images. Moreover, in this study we compile a large Hb electrophoresis image dataset, consisting of 103 strips containing 524 electrophoresis images with a clear consensus on the quality of electrophoresis obtained from 824 subjects. The proposed methodology is split into two parts: (1) single-patient electrophoresis image segmentation by means of the lane extraction technique, and (2) binary classification (normal or abnormal) of the electrophoresis images using state-of-the-art deep convolutional neural networks (CNNs) and using the concept of transfer learning. Image processing techniques including filtering and morphological operations are applied for object detection and lane extraction to automatically separate the lanes and classify them using CNN models. Seven different CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, SqueezeNet and MobileNetV2) were investigated in this study. InceptionV3 outperformed the other CNNs in detecting thalassaemia using Hb electrophoresis images. The accuracy, precision, recall, f1-score, and specificity in the detection of thalassaemia obtained with the InceptionV3 model were 95.8%, 95.84%, 95.8%, 95.8% and 95.8%, respectively. MobileNetV2 demonstrated an accuracy, precision, recall, f1-score, and specificity of 95.72%, 95.73%, 95.72%, 95.7% and 95.72% respectively. Its performance was comparable with the best performing model, InceptionV3. Since it is a very shallow network, MobileNetV2 also provides the least latency in processing a single-patient image and it can be suitably used for mobile applications. The proposed approach, which has shown very high classification accuracy, will assist in the rapid and robust detection of thalassaemia using Hb electrophoresis images.

A Rare Case of Multidrug-Resistant Tuberculosis Affecting the Pleura.

The majority of cases with tuberculous pleuritis have negative acid-fast bacilli (AFB) on smear microscopy, making the diagnosis difficult. This case report is based on the successful diagnosis and management of an extra-pulmonary (EP) multidrug-resistant tuberculosis (MDR-TB) patient with a history of lymphoma. Initial tests revealed a right-sided pleural effusion and thickening of the pleura. The closed pleural biopsy, pleural fluid histopathology, culture, and drug sensitivity testing (DST) report revealed Mycobacterium tuberculosis with isoniazid and rifampicin resistance. Based on the DST report, the patient was labeled as a case of MDR-TB and successfully managed with an individualized drug-resistant TB (DR-TB) regimen. With initial negative microscopy and GeneXpert MTB/RIF (Sunnyvale, CA: Cepheid Inc.) reports, this case demonstrated that DR-TB could exist even in the absence of risk factors. Furthermore, it also unveils the importance of line probe assays (LPAs) and culture in identifying MDR-TB. Lymphocytic/exudative pleural effusions and pleural biopsy specimens should be subjected early on to investigations like Xpert/MTB RIF, cultures, and genotypic DST to timely diagnose and treat DR-TB.

Hepatocellular carcinoma with extrahepatic blood supply from right renal artery.

Extrahepatic blood supply is seen in around 17-27% of hepatocellular carcinoma lesions. Evidence suggests that this extrahepatic supply most commonly originates from a right intercostal artery (70-83%) followed by left intercostal, omental and right renal arteries. Thus a comprehensive knowledge of variations in standard vascular anatomy and cognisance of factors influencing or predicting extrahepatic blood supply in HCC is instrumental in ensuring the success of surgical and interventional procedures. We present the unusual case of a 66-year-old male with HCC in Segment I of the liver with aberrant blood supply from the right renal artery in the absence of any risk factors for extrahepatic circulation. He successfully underwent transarterial chemoembolization. There was no evidence of residual disease on repeat imaging.

Seed germination and biochemical profile of Silybum marianum exposed to monometallic and bimetallic alloy nanoparticles.

In recent years nanotechnology has become increasingly important in almost every field. The new and improved physical, chemical and biological properties of material at nanoscale have far reaching implications in the fields of science and technology. Nanoparticles' effect on various plant species must be investigated to develop a comprehensive toxicity profile for nanoparticles. The current study strives to evaluate the effects of nine types of metal nanoparticles including monometallic and bimetallic alloy nanoparticles [Ag, Au, Cu, AgCu (1:3), AgCu (3:1), AuCu (1:3), AuCu (3:1), AgAu (1:3), AgAu (3:1)] on seed germination, root and shoot growth and biochemical profile of Silybum marianum plant. Seed germination was greatly affected and increased significantly upon treatment with nanoparticles' suspensions and was recorded highest for Ag nanoparticle suspension. Metal nanoparticles also had a significant effect on the biochemical profile of S. marianum. For the first week, the effect on DPPH, total phenolics content, total flavonoids content, total protein content, peroxidase activity and superoxide dismutase activity was enhanced, but declined as the time progressed. Among the nanoparticles being used, the effect of Ag nanoparticle was mostly enhancing. The results obtained are significant in mapping the effects of different monometallic and bimetallic nanoparticles on medicinal plant species.