Development and Validation of Novel HPLC Methods for Quantitative Determination of Vitamin D3 in Tablet Dosage Form.

In the present work, an efficient isocratic HPLC method was developed for the precise and accurate estimation of vitamin D3 in tablet form. The chromatographic conditions comprised an L3 silica column (5 µm in particle size, 4.6 mm × 250 mm) with a mobile phase n-hexane/ethyl acetate (85:15 v/v) with a flow rate of 2.0 mL/min and a detection wavelength of 292 nm. The new methodology was validated for accuracy, precision, specificity, robustness, and quantification limits according to an official monograph of USP/BP and ICH guidelines. The peak areas of the six replicates of the homogeneous sample were recorded. The mean value obtained was 67,301, and the relative standard deviation (RSD) was 0.1741. The linearity and range were in the acceptable bounds, i.e., 0.999, which was calculated using regression line analysis. The results show that the method is truly acceptable as the RSD, as the flow rate was 0.81%, while for the mobile phase composition, it was 0.72%, which lies in the acceptable range. The limit of detection (LOD) and the limit of quantification (LOQ) values were 0.0539 µg/mL and 0.1633 µg/mL, respectively. The % RSD of the intra and inter-day precision of the method was deemed acceptable according to the international commission for harmonization guidelines. The developed method has potential to be used for the detection and quantification of vitamin D3 during routine analysis for tablets in dosage form.

Pulmonary Rehabilitation for Post-COVID-19.

PURPOSE: Patients with COVID-19 often report persistent respiratory symptoms. Limited data exist on how to mitigate long-term sequelae of exercise intolerance and dyspnea. We aimed to study the role of pulmonary rehabilitation (PR) in patients with post-COVID-19. METHODS: This was an observational study. Consecutive patients with post-COVID-19, admitted to three separate outpatient PR programs, were enrolled. The program consisted of 8-12 wk of PR sessions (3 times/wk). Data were gathered at the initial visit and discharge. The primary outcome was the change in the 6-min walk test (6MWT) distance. Secondary outcomes included the Shortness of Breath Questionnaire (SOBQ), modified Borg dyspnea scale, Patient Health Questionnaire-9 (PHQ-9), and Lung Information Needs Questionnaire (LINQ). RESULTS: A total of 56 patients completed the PR program (age 62.8 ± 14.7 yr, 57% were men). At baseline, the mean 6MWT was 313.3 ± 193.8 m. On average, the 6MWT improved by 84.3 m after PR ( P < .0001). Apart from the modified Borg dyspnea scale, there was improvement across secondary outcomes: SOBQ (-16.9 points), PHQ-9 (-2.6 points), and LINQ (-4.2 points); all P < .05. CONCLUSION: Pulmonary rehabilitation showed a promising positive effect on patients with with post-COVID-19. It improved exercise capacity, perception of dyspnea, depressive symptoms, and patient knowledge needed to manage their lung disease. Pulmonary rehabilitation should be considered for post-COVID-19 patients.

CT-guided microwave ablation of the nonsurgical aspergilloma.

Background: Aspergillosis is a fungal infection that can lead to development of an aspergilloma, especially in patients with a history of cavitary lung disease. It is generally managed with antifungal therapy followed by surgical intervention. There are, however, limited options for the nonsurgical patient. Microwave ablation is already an effective, minimally invasive treatment being used in some lung malignancies and may be an alternative and definitive treatment in the inoperable patient. Methods: Two patients were considered for microwave ablation following their diagnoses of aspergillosis with hemoptysis. We sought to evaluate the efficacy of CT-guided microwave ablation of an aspergilloma in these patients who were not good candidates for surgical intervention. Results: Two male patients presented with hemoptysis and were found to have an aspergilloma. Case 1 was initially treated with antifungals and did not improve. He proceeded with VATS, and the procedure was aborted intraoperatively secondary to a frozen chest cavity. The patient subsequently elected to undergo CT-guided microwave ablation. He did not experience any immediate complications but was hospitalized for hemoptysis several weeks later. He developed alveolar hemorrhage and ultimately succumbed to PEA arrest.Case 2 was without hemoptysis at follow up and chose to pursue microwave ablation for definitive treatment. Case 2 developed post ablation pneumothorax requiring chest tube placement. Follow-up CT chest imaging was consistent with resolution of the aspergilloma. Conclusion: Microwave ablation is a safe and effective therapeutic approach in the treatment of lung malignancy with no severe or death related complications. There are almost no absolute contraindications. Microwave ablation may be utilized as a therapeutic option in the treatment of an aspergilloma in the non-surgical patient. This novel application may challenge the current gold standard of surgical intervention.

Cooperative Energy-Efficient Routing Protocol for Underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) contain sensor nodes that sense the data and then transfer them to the sink node or base station. Sensor nodes are operationalized through limited-power batteries. Therefore, improvement in energy consumption becomes critical in UWSNs. Data forwarding through the nearest sensor node to the sink or base station reduces the network's reliability and stability because it creates a hotspot and drains the energy early. In this paper, we propose the cooperative energy-efficient routing (CEER) protocol to increase the network lifetime and acquire a reliable network. We use the sink mobility scheme to reduce energy consumption by eliminating the hotspot issue. We have divided the area into multiple sections for better deployment and deployed the sink nodes in each area. Sensor nodes generate the data and send it to the sink nodes to reduce energy consumption. We have also used the cooperative technique to achieve reliability in the network. Based on simulation results, the proposed scheme performed better than existing routing protocols in terms of packet delivery ratio (PDR), energy consumption, transmission loss, and end-to-end delay.

Green synthesis, characterization, and antibacterial activity of silver nanoparticles using stem extract of Zanthoxylum armatum.

In this study, we report the green synthesis of silver nanoparticles (AgNPs) using Zanthoxylum armatum stem extract. The characteristic absorption at 385 nm suggested synthesis of AgNPs which was further confirmed by SEM, with a size in the range of 46.66 nm to 60.12 nm and a spherical shape, having an FCC structure, analyzed by XRD. FTIR analysis revealed the presence of phenol and secondary alcohol groups over the AgNPS. The elemental composition was further investigated by FESEM-EDX analysis which revealed the presence of silver in the synthesis nanoparticles. The synthesized silver nanoparticles exhibited antimicrobial activity against tested microorganisms with a zone of inhibition of 21 mm for Staphylococcus aureus, 17 mm for Pseudomonas aeruginosa, 18 mm for Salmonella enteric, and 18 mm for Escherichia coli. Overall, the results showed that the green silver nanoparticles could be safe, as they are capable of potential antimicrobial activity against S. aureus.

Green synthesis, characterization, antibacterial activity of metal nanoparticles and composite oxides using leaves extract of Ocimum basilicum L.

Nanoparticles plays a key role in the development of novel antibacterial substances against various pathogenic microorganisms. These nanoparticles due to their smaller size could be very effective as they can improve the antibacterial activity through lysis of bacterial cell wall. In the present research work, ZnO, MgO, NiO, AlO nanoparticles, and MgNiO, and AlZnO composite oxides were synthesized by green method from Ocimum basilicum leaves extract. The nanoparticles formed were evaluated using FTIR, XRD, EDX, and SEM to confirm the formation of NPs and to determine the morphology, elemental composition, shape and size, composition, and nature of bonds present in the NPs. Further, the NPs were tested for their antibacterial activity. In particular, ZnO NPs showed a good inhibitory effect against Pseudomonas aeruginosa with 20 mm zone of inhibition. Hence, the process reported herein could be optimized for large-scale preparation of NPs. RESEARCH HIGHLIGHTS: Green synthesis of ZnO, MgO, NiO, AlO nanoparticles, and MgNiO, and AlZnO composite oxides using Ocimum basilicum leaves extract NPs were characterized by various established characterization techniques like FTIR, XRD, EDX, and SEM. NPs showed antibacterial activity which was investigated by agar well diffusion method against Bacillus cereus, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. The method proved to be very simple, cost-efficient, and convenient.

Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network.

Wireless sensor network is widely used in different IoT-enabled applications such as health care, underwater sensor networks, body area networks, and various offices. A sensor node may face operational difficulties due to low computing capacity. Moreover, mobility has become an open challenge in the healthcare wireless body area network that is highly affected by message loss due to topological manipulation. In this article, an enhanced version of the well-known algorithm MT-MAC is proposed, namely DT-MAC, to ensure successful message delivery. It considers node handover mechanism among virtual clusters to ensure network integrity and also uses the concept of minimum connected dominating set for network formation to achieve efficient energy utilization. It is then compared with well-known algorithms such as MT-MAC. The simulation results show that an increase in little latency of roughly 3 percent in using the proposed protocol improves the MT-MAC's packet delivery by 13-17 percent and the response time by around 15 percent. Therefore, the algorithm is best fitted for real-time applications where the high packet delivery and response time are required.

Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model.

According to statistics, stroke is the second or third leading cause of death and adult disability. Stroke causes losing control of the motor function, paralysis of body parts, and severe back pain for which a physiotherapist employs many therapies to restore the mobility needs of everyday life. This research article presents an automated approach to detect different therapy exercises performed by stroke patients during rehabilitation. The detection of rehabilitation exercise is a complex area of human activity recognition (HAR). Due to numerous achievements and increasing popularity of deep learning (DL) techniques, in this research article a DL model that combines convolutional neural network (CNN) and long short-term memory (LSTM) is proposed and is named as 3-Layer CNN-LSTM model. The dataset is collected through RGB (red, green, and blue) camera under the supervision of a physiotherapist, which is resized in the preprocessing stage. The 3-layer CNN-LSTM model takes preprocessed data at the convolutional layer. The convolutional layer extracts useful features from input data. The extracted features are then processed by adjusting weights through fully connected (FC) layers. The FC layers are followed by the LSTM layer. The LSTM layer further processes this data to learn its spatial and temporal dynamics. For comparison, we trained CNN model over the prescribed dataset and achieved 89.9% accuracy. The conducted experimental examination shows that the 3-Layer CNN-LSTM outperforms CNN and KNN algorithm and achieved 91.3% accuracy.

Phytochemical screening, green synthesis of gold nanoparticles, and antibacterial activity using seeds extract of Ricinus communis L.

In the current research study, the phytochemical tests of the Ricinus communis L methanolic extract detected the presence of coumarin, reducing sugar, emodines, terpenoids, flavonoids, and steroids while water extract showed the presence of alkaloids, terpenoids, steroids, and soluble starch. The methanolic extract was further subjected to synthesize Au nanoparticles. The synthesized nanoparticles were confirmed by UV-Vis through the appearance of a peak at 550 nm which correspond to the existence of Au nanoparticles. The size and morphology of synthesized nanoparticle were further studied by the dynamic light scattering technique which shows that nanoparticles were in the range of 100 nm which were further confirmed by transmission electron microscopy. The synthesized nanoparticles and extracts were tested against different bacterial strains. The results revealed that methanolic extract showed maximum inhibition of 19.5 mm against Klebsiella pneumoniae and 20.33 mm against Bacillus cereus while water extract showed maximum inhibition of 18.16 mm against Pseudomonas aeruginosa and 19.33 mm against B. cereus. On the other hand, Au nanoparticles showed maximum inhibition of 18.5 mm against Escherichia coli.

RM-ADR: Resource Management Adaptive Data Rate for Mobile Application in LoRaWAN.

LoRaWAN is renowned and a mostly supported technology for the Internet of Things, using an energy-efficient Adaptive Data Rate (ADR) to allocate resources (e.g., Spreading Factor (SF)) and Transmit Power (TP) to a large number of End Devices (EDs). When these EDs are mobile, the fixed SF allocation is not efficient owing to the sudden changes caused in the link conditions between the ED and the gateway. As a result of this situation, significant packet loss occurs, increasing the retransmissions from EDs. Therefore, we propose a Resource Management ADR (RM-ADR) at both ED and Network Sides (NS) by considering the packet transmission information and received power to address this issue. Through simulation results, RM-ADR showed improved performance compared to the state-of-the-art ADR techniques. The findings indicate a faster convergence time by minimizing packet loss ratio and retransmission in a mobile LoRaWAN network environment.

The Effect of AlI3 Nanoadditive on the Thermal Behavior of PMMA Subjected to Thermoanalytical Py-GC-MS Technique.

Thermal degradation of polymethylmethacrylate (PMMA) was studied by using inorganic salt of aluminum triiodide (AlI3). The composites of PMMA were prepared with AlI3 by changing the concentration of the AlI3 additive from 2% to 10% (w/w). The PMMA composites with AlI3 were characterized by TGA, DTG, SEM, FTIR, HBT, and Py-GC-MS techniques. The FTIR peaks of PMMA composite at 1316, 786, and 693 cm-1 justify the chemical association between PMMA and AlI3. TGA study shows that the stability of PMMA is enhanced by the addition of the AlI3 additive. SEM analysis represented that there is a relationship between polymer and additive when they are mixed at the molecular level. The horizontal burning test (HBT) also confirmed that the AlI3 additive produced the flame retarding properties in PMMA polymer. The burning rate of composite with 10% of AlI3 additive decreases five times as much as compared to pure PMMA polymer. Py-GC-MS analysis deduced that PMMA composite produced less toxic and environment-friendly substances (CO2) by the influence of AlI3 additive as compared to neat PMMA.

Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning.

Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.

Density functional theory, molecular docking and bioassay studies on (S)-2-hydroxy-N-(2S,3S,4R,E)-1,3,4 trihydroxyicos-16-en-2-yl)tricosanamide.

A novel indigoferamide-A, earlier isolated from the seeds of Indigofera heterantha Wall was characterized using density functional theory, molecular docking and bioassays studies. Density functional theory calculations were performed at B3LYP/6-31G(d,p) to gain geometric insight of the compound. Conformational analyses have been performed around three important dihedral angles to explore the lowest energy structure and conformer. The simulated vibrational spectrum of the compound at B3LYP/6-31G(d,p) was scaled with two scaling factors, and the scaled harmonic vibrations shows nice correlation with the experimental values. 1H and 13C NMR chemical shifts were calculated using Cramer's re-parameterized function W04 at 6- 31G(d,p) basis set. Several conformers lying within 2 kcal mol-1 of the minimum energy conformer were considered; however, the chemical shifts were not significantly different among these conformers. The Gaussian averaged theoretical 1H and 13C chemical shifts correlate nicely with the experimental data. Electronic properties such as band gap, ionization potential and electron affinities were also simulated for the first time, however, no comparison could be made with the experiment. The compound was also screened for urease, antiglycation activities and the theoretical explanation of the results is provided based on molecular docking simulations.

Spectroscopic and density functional theory studies of 7-hydroxy-3'-methoxyisoflavone: A new isoflavone from the seeds of Indigofera heterantha (Wall).

A new isoflavone 7-hydroxy 3'-methoxyisoflavone (1) is isolated from the seeds of Indigofera heterantha. The structure of this new compound was established using spectroscopic techniques such as ID, 2D NMR, and mass spectrometry. Density functional theory calculations are carried out for the first time for geometric, electronic and spectroscopic properties of 1 (isoflavone). DFT calculations have been performed at B3LYP/6-311G(d,p) level of theory for obtaining geometric and spectroscopic properties of compound 1. The simulated vibrational spectrum of compound 1 at B3LYP/6-311G(d,p) shows nice correlation with the experimental IR spectrum after a scaling factor of 0.973. (1)H and (13)C NMR chemical shifts were calculated using Cramer's re-parameterized function WP04 at 6-311G(d,p) basis set, and show nice correlation with the experimental data. Four conformers were considered for NMR chemical shift calculations. Electronic properties such as band gap, Ionization potential and electron affinities were also simulated for the first time; however, no comparison could be made with the experiment.

Genotyping of HCV RNA reveals that 3a is the most prevalent genotype in mardan, pakistan.

The clinical outcomes of patients infected with hepatitis C virus (HCV) range from acute resolving hepatitis to chronic liver diseases such as liver cirrhosis or hepatocellular carcinoma. Identification of the infecting virus genotype is indispensable for the exploration of many aspects of HCV infection, including epidemiology, pathogenesis, and response to antiviral therapy. 1419 individuals were screened for anti-HCV in this study, of which 166 (11.7%) were found reactive by ICT (Immunochromatographic test). These 166 anti-HCV positive and 26 normal individuals were further analyzed. RNA was extracted from serum and reverse-transcribed to cDNA and the core region of HCV genome was targeted and amplified by multiplex PCR. HCV RNA was detected in 121 individuals, of which 87 were male and 34 were female. Genotype 3a was the most prevalent among all the genotypes observed followed by 3b. Genotypes 1a, 2a, and 2b were found in 10.89%, 13.22%, and 6.61% patients, respectively. 25.41% of the HCV RNA positive samples were not typed. 6.05% of patients were found having mixed genotypes. These findings will not only help the physicians to prescribe more appropriate treatment for the HCV infection but will also draw the attention of health-related policy makers to devise strategies to curb the disease more effectively.