Exploring the antimicrobial and cytotoxic potential of novel chloroquine analogues.

Aim: To synthesize novel chloroquine analogues and evaluate them for antimicrobial and cytotoxic potential. Methods: Novel analogues were synthesized from chloroquine by nucleophilic substitution reaction at the 4-amino position. Results: Analogue CS1 showed maximum antimicrobial potential (30.3 ± 0.15 mm zone) against Pseudomonas aeruginosa and produced a 19.2 ± 0.21 mm zone against Candida albicans, while CS0 produced no zone at the same concentration. Analogue CS9 has excellent cytotoxic potential (HeLa cell line), showing 100% inhibition (IC50 = 8.9 ± 1.2 µg/ml), compared with CS0 (61.9% inhibition at 30 µg/ml). Conclusion: These synthesized chloroquine analogues have excellent activity against different microbial strains and cervical cancer cell lines (HeLa) compared with their parent molecule.

Formulation and characterization of glipizide solid dosage form with enhanced solubility.

Glipizide, a poor water-soluble drug belongs to BCS class II. The proposed work aimed to enhance the solubility of glipizide by preparing solid dispersions, using polyvinyl pyrrolidone (PVP) and polyethylene glycol (PEG). Solvent evaporation method was used for the preparation of glipizide solid dispersions. Solid dispersions were prepared in four different drug-to-polymer ratios i.e. 1:1, 1:2, 1:3 and 1:4. Mainly effect of three polymers (PVP K30, PVP K90 and PEG 6000) was evaluated on the solubility and dissolution of glipizide. The in-vitro dissolution of all prepared formulations was performed under pH 6.8 at 37°C using USP type II apparatus. In-vitro dissolution results revealed that the formulations having high concentrations of the polymer showed enhanced solubility. Enhancements in the solubility and rate of dissolution of the drug were noted in solid dispersion formulations compared to the physical blends and pure drug. Solid dispersions containing polyvinyl pyrrolidone exhibited a more favorable pattern of drug release compared to the corresponding solid dispersions with PEG. An increase in the maximum solubility of the drug within the solid dispersion systems was observed in all instances. Two solid dispersion formulations were optimized and formulated into immediate-release tablets, which passed all the pharmacopoeial and non-pharmacopoeial tests. Fourier transformed Infrared (FTIR) spectroscopy X-ray diffraction (XRD) and Differential scanning calorimetry (DSC) were used to indicate drug: polymer interactions in solid state. Analysis of the solid dispersion samples through characterization tests indicated the compatibility between the drug and the polymer.

Self-supervised learning for inter-laboratory variation minimization in surface-enhanced Raman scattering spectroscopy.

Surface-enhanced Raman scattering (SERS) spectroscopy is still considered poorly reproducible despite its numerous advantages and is not a sufficiently robust analytical technique for routine implementation outside of academia. In this article, we present a self-supervised deep learning-based information fusion technique to minimize the variance in the SERS measurements of multiple laboratories for the same target analyte. In particular, a variation minimization model, coined the minimum-variance network (MVNet), is designed. Moreover, a linear regression model is trained using the output of the proposed MVNet. The proposed model showed improved performance in predicting the concentration of the unseen target analyte. The linear regression model trained on the output of the proposed model was evaluated by several well-known metrics, such as root mean square error of prediction (RMSEP), BIAS, standard error of prediction (SEP), and coefficient of determination (R2). The leave-one-lab-out cross-validation (LOLABO-CV) results indicate that the MVNet also minimizes the variance of completely unseen laboratory datasets while improving the reproducibility and linear fit of the regression model. The Python implementation of MVNet and the code for the analysis can be found on the GitHub page https://github.com/psychemistz/MVNet.

Tunable image quality control of 3-D ultrasound using switchable CycleGAN.

In contrast to 2-D ultrasound (US) for uniaxial plane imaging, a 3-D US imaging system can visualize a volume along three axial planes. This allows for a full view of the anatomy, which is useful for gynecological (GYN) and obstetrical (OB) applications. Unfortunately, the 3-D US has an inherent limitation in resolution compared to the 2-D US. In the case of 3-D US with a 3-D mechanical probe, for example, the image quality is comparable along the beam direction, but significant deterioration in image quality is often observed in the other two axial image planes. To address this, here we propose a novel unsupervised deep learning approach to improve 3-D US image quality. In particular, using unmatched high-quality 2-D US images as a reference, we trained a recently proposed switchable CycleGAN architecture so that every mapping plane in 3-D US can learn the image quality of 2-D US images. Thanks to the switchable architecture, our network can also provide real-time control of image enhancement level based on user preference, which is ideal for a user-centric scanner setup. Extensive experiments with clinical evaluation confirm that our method offers significantly improved image quality as well user-friendly flexibility.

Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network.

Ultrasound (US) imaging is a mature technology that has widespread applications especially in the healthcare sector. Despite its widespread use and popularity, it has an inherent disadvantage that ultrasound images are prone to speckle and other kinds of noise. The image quality in the low-cost ultrasound imaging systems is degraded due to the presence of such noise and low resolution of such ultrasound systems. Herein, we propose a method for image enhancement where, the overall quality of the US images is improved by simultaneous enhancement of US image resolution and noise suppression. To avoid over-smoothing and preserving structural/texture information, we devise texture compensation in our proposed method to retain the useful anatomical features. Moreover, we also utilize US image formation physics knowledge to generate augmentation datasets which can improve the training of our proposed method. Our experimental results showcase the performance of the proposed network as well as the effectiveness of the utilization of US physics knowledge to generate augmentation datasets.

Chitosan/guar gum-based thermoreversible hydrogels loaded with pullulan nanoparticles for enhanced nose-to-brain drug delivery.

The biopolymers-based two-fold system could provide a sustained release platform for drug delivery to the brain resisting the mucociliary clearance, enzymatic degradation, bypassing the first-pass hepatic metabolism, and BBB thus providing superior bioavailability through intranasal administration. In this study, poloxamers PF-127/PF-68 grafted chitosan HCl-co-guar gum-based thermoresponsive hydrogel loaded with eletriptan hydrobromide laden pullulan nanoparticles was synthesized and subjected to dynamic light scattering, Fourier transform infrared spectroscopy, thermal analysis, x-ray diffraction, scanning electron microscopy, stability studies, mucoadhesive strength and time, gel strength, cloud point assessment, rheological assessment, ex-vivo permeation, cell viability assay, histology studies, and in-vivo Pharmacokinetics studies, etc. It is quite evident that CSG-EH-NPs T-Hgel has an enhanced sustained release drug profile where approximately 86 % and 84 % of drug released in phosphate buffer saline and simulated nasal fluid respectively throughout 48 h compared to EH-NPs where 99.44 % and 97.53 % of the drug was released in PBS and SNF for 8 h. In-vivo PKa parameters i.e., mean residence time (MRT) of 11.9 ± 0.83 compared to EH-NPs MRT of 10.2 ± 0.92 and area under the curve (AUCtot) of 42,540.5 ± 5314.14 comparing to AUCtot of EH-NPs 38,026 ± 6343.1 also establish the superiority of CSG-EH-NPs T-Hgel.

Development, characterization and In-vitro evaluation of guar gum based new polymeric matrices for controlled delivery using metformin HCl as model drug.

Currently, hydrogels are considered as ideal biomaterials due to their unique structure and characteristics that facilitates considerable hydrophilicity, swelling, drug loading and release. In this study, we report pH-responsive GG-MAA-AMPS hydrogel delivery system prepared via free radical polymerization technique. Hydrogels were loaded with Metformin HCl as a model drug. Hydrogels were characterized through Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), X-ray diffraction (XRD) and scanning electron microscopy (SEM). FTIR confirmed the successful crosslinking of reactants, hydrogel network formation and drug loading. TGA and DSC proved the higher thermal stability of reactants after crosslinking and drug loading. XRD analysis showed decrease in crystallinity of drug after loading into the hydrogels. SEM revealed smooth and glassy appearance of both loaded and unloaded hydrogels. Gel content was increased with increase in concentration of reactants. Drug entrapment was decreased by increasing concentration of GG and AMPS while MAA acted inversely. Hydrogels displayed pH-dependent swelling and drug release behavior being high at pH 6.8 and 7.4 while low at acidic pH (1.2). Oral tolerability in rabbits showed that hydrogels were safe without causing any hematological or histopathological changes in healthy rabbits. Based on the obtained results, GG-MAA-AMPS can be considered as potential carrier for metformin HCl as well as other hydrophilic drugs.

Exploring the factors and barriers of healthcare professionals in tertiary care hospitals toward pharmacovigilance: a multicenter study from Khyber Pakhtunkhwa, Pakistan.

BACKGROUND: Spontaneous Adverse drug reactions (ADRs) reporting is a cornerstone for a successful pharmacovigilance program as under-reporting of ADRs remains a major issue around the globe. The current study aimed to assess the knowledge attitude and practices of health care professionals regarding pharmacovigilance along with barriers and factors to encourage ADR reporting at tertiary care hospitals of Khyber-Pakhtunkhwa, Pakistan. METHODS: A questionnaire-based cross-sectional survey was conducted, using the convenience sampling method to collect the data from doctors, nurses, and pharmacists working in seven tertiary care hospitals from seven districts of Khyber-Pakhtunkhwa province, Pakistan, between July 2019 and March 2020. RESULTS: During the study, a total of 830 questionnaires were distributed, out of which 669 were returned (response rate 80.6%). Overall, Healthcare professionals exhibited poor knowledge (79.5%) about ADR reporting and pharmacovigilance however, 73.5% of pharmacists were more knowledgeable as compared to 18.7% doctors and 13.8% nurses (p < .001). Moreover, poor reporting practices were displayed by 95.6% doctors, 94.4% nurses, 94.4 and 75.5% pharmacists (p < .001). However, the majority of healthcare professionals showed an overall positive attitude (94%) toward ADR reporting. The most frequently cited barriers were unavailability of reporting forms (92.5%), absence of a professional environment to discuss ADRs (82.5%), and lack of training (81.8%) whereas, most common factors to encourage ADR reporting were obligatory reporting (85.9%) and provision of ADR management guidelines and training (84.3%). A significant relation was found between the healthcare professionals and their professional status with the overall knowledge, attitude, and practice (KAP) scores (p < .001) whereas a medium, positive correlation was found between the knowledge and practice of pharmacovigilance and ADR reporting by the healthcare professionals (r = 0.321, n = 669, p < .001). CONCLUSION: There is an overall lack of knowledge and poor reporting practices among health care professionals on ADR reporting and pharmacovigilance. Hence the study suggests that strategies should be devised by all the stakeholders to properly educate and train the healthcare professionals in this area to enhance overall patient safety and safe use of medicines.

Assessment of without prescription antibiotic dispensing at community pharmacies in Hazara Division, Pakistan: A simulated client's study.

Antibiotics dispensing without a prescription is an irrational practice and can increase the risk of antibiotic resistance, which is a significant public health concern around the globe. This study was aimed to determine the extent to which antibiotics are supplied without prescription in the community pharmacies (CPs) at Hazara Division from November 2020 to February 2021. The simulated client method (SCM) was used, and the data were gathered, recorded, and analyzed through different statistical methods with descriptive and inferential techniques. The antibiotic dispensing was observed in CPs (90.5%), the most dispensed antibiotics were azithromycin (29.4%) and ciprofloxacin (46.5%) respectively. Furthermore, visited medical stores/ drug outlets, 9.5% of the visited stores denied dispensing of antibiotics because they preferred a referral to visit physicians (23. 9%) and (12.8%) did not had the antibiotics at the time of visits. Antibiotics were more obtainable in retail medical stores (AOR = 8.6, 95 percent Cl: 3.0-24.7; p = 0.001) than in pharmacies. In rural areas antibiotics dispensing was more (p = 0.004) as compared to urban areas. Staff members also had asked about patient's (17.7%) symptoms and drug allergies (12.3% and 3.9%), and (1.5%) they consoled them about their medications. The findings of this study indicate that nonprescription antibiotic sales are very common, despite national rules prohibiting this activity. When the simulated Client requested for any medication to relieve his or her discomfort, many antibiotics were given out without a prescription. Pharmacies/medical stores in Hazara Division selling antibiotics without a prescription are worrying and need immediate action by regulators.

Machine Learning-Based Heavy Metal Ion Detection Using Surface-Enhanced Raman Spectroscopy.

Surface-Enhanced Raman Spectroscopy (SERS) is often used for heavy metal ion detection. However, large variations in signal strength, spectral profile, and nonlinearity of measurements often cause problems that produce varying results. It raises concerns about the reproducibility of the results. Consequently, the manual classification of the SERS spectrum requires carefully controlled experimentation that further hinders the large-scale adaptation. Recent advances in machine learning offer decent opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are missing. Towards this end, we provide the SERS spectral benchmark dataset of lead(II) nitride (Pb(NO3)2) for a heavy metal ion detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. The proposed model can successfully identify the Pb(NO3)2 molecule from SERS measurements of independent test experiments. In particular, the proposed model shows an 84.6% balanced accuracy for the cross-batch testing task.

Switchable and Tunable Deep Beamformer Using Adaptive Instance Normalization for Medical Ultrasound.

Recent proposals of deep learning-based beamformers for ultrasound imaging (US) have attracted significant attention as computational efficient alternatives to adaptive and compressive beamformers. Moreover, deep beamformers are versatile in that image post-processing algorithms can be readily combined. Unfortunately, with the existing technology, a large number of beamformers need to be trained and stored for different probes, organs, depth ranges, operating frequency, and desired target 'styles', demanding significant resources such as training data, etc. To address this problem, here we propose a switchable and tunable deep beamformer that can switch between various types of outputs such as DAS, MVBF, DMAS, GCF, etc., and also adjust noise removal levels at the inference phase, by using a simple switch or tunable nozzle. This novel mechanism is implemented through Adaptive Instance Normalization (AdaIN) layers, so that distinct outputs can be generated using a single generator by merely changing the AdaIN codes. Experimental results using B-mode focused ultrasound confirm the flexibility and efficacy of the proposed method for various applications.

SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network.

Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are lacking. Towards this end, we provide the SERS spectral benchmark dataset of Rhodamine 6G (R6G) for a molecule detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. Our best model, coined as the SERSNet, robustly identifies R6G molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task.

ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs.

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab-based methods are reliable; however, they are expensive and time consuming and are, therefore, not scalable. In this research, we propose a sequence-based novel machine learning approach for the prediction of ECM proteins. In the proposed method, composition of k-spaced amino acid pair (CKSAAP) features are encoded into a classifiable latent space (LS) with the help of deep latent space encoding (LSE). A comprehensive ablation analysis is conducted for performance evaluation of the proposed method. Results are compared with other state-of-the-art methods on the benchmark dataset, and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.

AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features.

It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computational methodology based on the notion of deep latent space encoding. A deep neural network classifier fused with an auto-encoder learns class labels in a pruned latent space. This strategy has eliminated the need to separately develop classifier and the feature selection model, allowing the standalone model to effectively harness discriminating feature space and perform improved predictions. A thorough analytical study has been presented alongwith the PCA/tSNE visualization and PCA-GCNR scores to show the discriminating power of the proposed method. The proposed method showed a high MCC value of 0.43 and a balanced accuracy of 76.2%, which is superior to the existing models. The model has been evaluated on an independent dataset during which it outperformed the contemporary methods by correctly identifying the novel proteins with an accuracy of 95%.

An Exploration of Knowledge, Attitude, and Practices of Physicians toward Pharmacovigilance at Tertiary Care Hospitals in Khyber-Pakhtunkhwa Province, Pakistan.

Drug safety assures the effectiveness, safety, and security of drugs, vaccines, and other biologicals to protect public health. Medication-related errors coupled with unjudicial medication practices often cause a catastrophic impact on the healthcare system globally. The present study aimed to assess the knowledge, attitude, and practice of physicians toward pharmacovigilance and barriers to adverse drug reaction (ADR) reporting at tertiary care hospitals in Khyber-Pakhtunkhwa (KP) province, Pakistan. A descriptive cross-sectional study was conducted among registered doctors working in seven tertiary care hospitals from seven administrative divisions of KP province of Pakistan from July 2019 to March 2020. During the study period, 358 physicians who fulfilled the inclusion criteria and agreed to participate completed and returned the validated structured questionnaires. Descriptive and inferential statistics were applied for data analysis. The majority of physicians had poor knowledge (81.3%) regarding pharmacovigilance along with poor reporting practices (94.9%), although (96.5%) had a positive attitude toward ADR reporting. A significant barrier identified was the unavailability of reporting forms (95.9%), whereas mandatory ADR reporting (96.2%) was the major factor to encourage ADR reporting. Physicians aged ≥ 41 and experience ≥ 11 years had significantly more knowledge than other categories (P < 0.001). Significant association (P < 0.001) of physicians' knowledge and practice were found where 77.2% of the participants having poor knowledge reported poor practices. Physicians' understanding of pharmacovigilance was suboptimal, although they have a positive attitude toward ADR reporting. Thus, there is a need for continuous education and training programs to support pharmacovigilance activities that could improve physicians' understanding.

Improved drug delivery and accelerated diabetic wound healing by chondroitin sulfate grafted alginate-based thermoreversible hydrogels.

Injectable hydrogels with multifunctional tunable properties comprising biocompatibility, anti-oxidative, anti-bacterial, and/or anti-infection are highly preferred to efficiently promote diabetic wound repair and its development remains a challenge. In this study, we report chondroitin sulphate (CS) and sodium alginate (SA)-based injectable hydrogel using solvent casting method loaded with curcumin that could potentiate reepithelization, increase angiogenesis, and collagen deposition at wound microenvironment to endorse healing cascade. The physical interaction and self-assembly of chondroitin sulfate grafted alginate (CS-Alg-g-PF127) hydrogel were confirmed using nuclear magnetic resonance (1H NMR) and Fourier transformed infrared spectroscopy (FT-IR), and cytocompatibility was confirmed by fibroblast viability assay. The Masson's trichrome (MT) and hematoxylin and eosin (H&E) results revealed that blank chondroitin sulfate grafted alginate (CS-Alg-g-PF127) and CUR loaded CS-Alg-g-PF127 hydrogel had promising tissue regenerative ability, and showing enhanced wound healing compared to other treatment groups. The controlled release of CUR from injectable hydrogel was evaluated by drug release studies and pharmacokinetic profile (PK) using high-performance liquid chromatography (HPLC) that exhibited the mean residence time (MRT) and area under the curve (AUC) was increased up to 16.18 h and 203.64 ± 30.1 µg/mL*h, respectively. Cytotoxicity analysis of the injectable hydrogels using 3 T3-L1 fibroblasts cells and in vivo toxicity evaluated by subcutaneous injection for 24 h followed by histological examination, confirmed good biocompatibility of CUR loaded CS-Alg-g-PF127 hydrogel. Interestingly, the results of in vivo wound healing by injectable hydrogel showed the upregulation of fibroblasts-like cells, collagen deposition, and differentiated keratinocytes stimulating dermo-epidermal junction, which might endorse that they are potential candidates for excisional wound healing models.

Synthesis, Characterization and Safety Evaluation of Sericin-Based Hydrogels for Controlled Delivery of Acyclovir.

Conventional formulations of antiviral drug acyclovir have various limitations such as low bioavailability. The current study was aimed at developing polymeric matrices for the controlled delivery of acyclovir using sericin as polymer and acrylic acid (AA) as a monomer. The free radical polymerization technique was used for hydrogel formulation. Briefly, sericin was chemically cross-linked with acrylic acid. N'-N'-methylene bis-acrylamide (MBA) and ammonium persulfate (APS) were used as cross-linker and initiator, respectively. FTIR spectra showed that acyclovir was successfully loaded into sericin hydrogel. SEM micrographs revealed that the outer surface was solid-like and smooth. According to DSC thermograms, the developed polymeric network was thermally stable. Amorphous nature of acyclovir was observed in XRD. The pH of medium and reactants' concentration affected swelling dynamics and acyclovir release pattern. In addition, drug release occurred through a diffusion-controlled process. Sericin hydrogel suspension was well tolerable up to 3800 mg/kg of rabbits' body weight. Haematology and serum chemistry results were well within the range signifying normal liver and kidney functions. Similarly, histopathology slides of the rabbit's vital organs were also in normal condition without causing any histopathological change. It was concluded from the findings that sericin-co-AA polymeric matrices are ideal for the pH-dependent delivery of acyclovir.

Selective or Routine Histology of Cholecystectomy Specimens for Diagnosing Incidental Carcinoma of Gallbladder and Correlation with Careful Intraoperative Macroscopic Examination? A Systematic Review.

BACKGROUND: Selective or Routine histology of cholecystectomy specimens for benign gallbladder disease has always been a matter of debate because of the low prevalence and bad prognosis associated with gall bladder carcinoma. The objective of this study is to ascertain whether selective histology can be preferred over Routine histology without any harm. METHODS: This systematic review is conducted according to PRISMA's checklist; relevant articles were searched in the database until September 1 2020 in PubMed, Scopus, Science Direct, and Web of Science databases, manually, with search queries and without date restrictions. Studies included in this systematic review involved patients who underwent cholecystectomy for benign gallbladder disease and were diagnosed with gallbladder carcinoma incidentally either after selective or routine histology of the gallbladder. RESULTS: A total of 24 routine or selective histology recommending studies were selected for the systematic review after following the inclusion and exclusion criteria. These studies comprised 77,213 numbers of patients and 486 numbers of Malignancies. These studies correlate the number of IGBC diagnosed histologically with the number of IGBC's that were suspected by the surgeons intraoperative by macroscopy. Routine recommending studies show a significant number of IGBC diagnosed histologically as missed by surgeons whereas the selective recommending studies show most of the histologically diagnosed IGBC already suspected by the surgeons intraoperative. When comparing the macroscopic details of the IGBC's between routine and selective studies, we found that there was significant overlap. Most of the findings missed by the surgeons as suspicious in routine studies were suspected by the surgeons involved in selective histology recommending studies. Thereby, favouring selective histology and emphasizing the need for careful intraoperative macroscopy for suspecting IGBC. CONCLUSION: Selective Histological examination of cholecystectomy specimens can be preferred if a careful intraoperative macroscopic examination is done and patient risk factors are taken into consideration.

Variational Formulation of Unsupervised Deep Learning for Ultrasound Image Artifact Removal.

Recently, deep learning approaches have been successfully used for ultrasound (US) image artifact removal. However, paired high-quality images for supervised training are difficult to obtain in many practical situations. Inspired by the recent theory of unsupervised learning using optimal transport driven CycleGAN (OT-CycleGAN), here, we investigate the applicability of unsupervised deep learning for US artifact removal problems without matched reference data. Two types of OT-CycleGAN approaches are employed: one with the partial knowledge of the image degradation physics and the other with the lack of such knowledge. Various US artifact removal problems are then addressed using the two types of OT-CycleGAN. Experimental results for various unsupervised US artifact removal tasks confirmed that our unsupervised learning method delivers results comparable to supervised learning in many practical applications.