Determining the cost of acute stroke care for a single stay at a tertiary care centre in Pakistan.

OBJECTIVE: To assess the economic burden of acute stroke, and to determine the average cost of acute stroke care for a single hospital stay in a public tertiary care hospital. METHODS: The cross-sectional study was conducted at the Medical Teaching Institute, Bacha Khan Medical Complex, Swabi, Pakistan, from May 16 to September 19, 2022, and comprised patients of either gender who were hospitalised with an acute stroke for the first time. All costs incurred during the care of the patients were measured using the micro-costing methodology, and the association of the cost with other variables was evaluated. Data was analysed using SPSS 24. RESULTS: Of the 34 patients, 24(70.6%) were males and 10(29.4%) were females. The overall mean age was 66+/-13.00 years. The mean length of hospital stay was 4+/-3.00 days. The mean total cost was 18,156+/-9,068 Pakistani rupees, which was the equivalent of 76.89+/-38.4 United States dollars. The cost of the first day of admission was the highest, declining per day as the stay progressed, and imaging/laboratory investigations formed the highest component of the overall cost (p<0.001). CONCLUSIONS: The cost of acute stroke care was found to be high even in a public hospital. The length of hospital stay was the most important determinant of the overall cost.

An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics.

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.

Bioaccumulation Pattern and Health Risk Assessment of Heavy Metals in Cirrhinus mrigala at Panjnad Headworks, Bahawalpur, Pakistan.

Heavy metal accumulation in freshwater ecosystem has become one of the major aquatic environmental concerns for freshwater flora and fauna due to their higher stability and bioaccumulation as well as bio-magnification properties. Furthermore, passing through the food web, these heavy metals affect human populations ultimately. This study assessed the heavy metal accumulation in Cirrhinus mrigala in spring, autumn, and winter at different locations (I, II, and III) of Panjnad headwork. Furthermore, the human health risk assessment for the consumption of C. mrigala from the sampling locations was also carried out. Fish were collected from upper (I), middle (II), and lower (III) stream of Panjnad on a monthly basis. The current study evaluated the accumulation of Aluminum (Al), Arsenic (As), Barium (Ba), and Lead (Pb) in various fish organs (liver, kidney, gills, fins, skin, muscles and bones) and assessed their potential hazard to human health through health risk assessment indicators. The results demonstrated a significant difference (p < 0.05) in heavy metal accumulation in different fish organs, seasons, and locations. The accumulation of Al, As, Ba, and Pb were considerably higher in liver and kidney as compared to the other body organs and followed a trend of liver > kidney > gills > fins > skin > bones > muscle and the overall mean concentrations of metals in different body tissues of C. mrigala were in the order of Al > As > Ba > Pb. The results also concluded that C. mrigala caught from the Panjnad headwork is not safe for human consumption due to higher values of TTHQIng (3.76), THQIng for Ba (3.27) and CRIng for As (6.4742).

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing.

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.

Fabrication and in vitro evaluation of chitosan-gelatin based aceclofenac loaded scaffold.

Scaffold development is a nascent field in drug development. The scaffolds mimic the innate microenvironment of the body. The goal of this study was to formulate a biocompatible and biodegradable scaffold, loaded with an analgesic drug, aceclofenac (Ace). The bioscaffold is aimed to have optimum mechanical strength and rheology, with drug released in a sustained manner. It was prepared via chemical cross-linking method: a chitosan (CS) solution was prepared and loaded with Ace; gelatin (GEL) was added and the mixture was cross-linked to get a hydrogel. 20 formulations were prepared to optimize different parameters including the stirring speed, drug injection rate and crosslinker volume. The optimal formulation was selected based on the viscosity, drug solubility, homogeneity, porosity and swelling index. A very high porosity and swelling index were attained. In vitro release data showed sustained drug delivery, with effective release at physiological and slightly acidic pH. SEM analysis revealed a homogeneous microstructure with highly interconnected pores within an extended polymer matrix. FT-IR spectra confirmed the absence of polymer-drug interactions, XRD provided evidences for efficient drug entrapment within the scaffold. Rheological analysis corroborated the scaffold injectability. Mathematical models were applied to in-vitro data, and the best fit was attained with Korsmeyer-Peppas.

Fluorescent and Phosphorescent Nitrogen-Containing Heterocycles and Crown Ethers: Biological and Pharmaceutical Applications.

Fluorescent molecules absorb photons of specific wavelengths and emit a longer wavelength photon within nanoseconds. Recently, fluorescent materials have been widely used in the life and material sciences. Fluorescently labelled heterocyclic compounds are useful in bioanalytical applications, including in vivo imaging, high throughput screening, diagnostics, and light-emitting diodes. These compounds have various therapeutic properties, including antifungal, antitumor, antimalarial, anti-inflammatory, and analgesic activities. Different neutral fluorescent markers containing nitrogen heterocycles (quinolones, azafluoranthenes, pyrazoloquinolines, etc.) have several electrochemical, biological, and nonlinear optic applications. Photodynamic therapy (PDT), which destroys tumors and keeps normal tissues safe, works in the presence of molecular oxygen with light and a photosensitizing drugs (dye) to obtain a therapeutic effect. These compounds can potentially be effective templates for producing devices used in biological research. Blending crown compounds with fluorescent residues to create sensors has been frequently investigated. Florescent heterocyclic compounds (crown ether) increase metal solubility in non-aqueous fluids, broadening the application window. Fluorescent supramolecular polymers have widespread use in fluorescent materials, fluorescence probing, data storage, bio-imaging, drug administration, reproduction, biocatalysis, and cancer treatment. The employment of fluorophores, including organic chromophores and crown ethers, which have high selectivity, sensitivity, and stability constants, opens up new avenues for research. Fluorescent organic compounds are gaining importance in the biological world daily because of their diverse functionality with remarkable structural features and positive properties in the fields of medicine, photochemistry, and spectroscopy.

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Optimize PLA/EVA Polymers Blend Compositional Coating for Next Generation Biodegradable Drug-Eluting Stents.

In this research work, polymer blends of poly-lactic acid (PLA)/ethylene vinyl acetate (EVA) were prepared as the drug carrier materials for a bi-layer drug-loaded coating film for coronary stents. Different optimum compositions of blends were prepared by using an intense mixer. Then, the blends were hot-pressed and later cold-pressed to prepare for films of different thickness. The changes in weight, surface analysis and biodegradability with increasing time were studied using Scanning electron microscopy (SEM), weight loss and biodegradability tests. The mechanical and thermal properties of drug-loaded films were studied through universal testing machine (UTM) and thermo-gravimetric analysis (TGA). The effects of PLA, EVA and drug contents on in-vitro drug contents were investigated through the Ultraviolet-Visible Spectroscopy (UV-VIS) chemical analysis technique. The results obtained clearly showed that the addition of PLA promoted the unleashing of the drug whereas the addition of EVA nearly did not have the same affect. The mechanical properties of these various films can be tuned by adjusting the contents of blend parts. The factors affecting the unleashing of the drug became a serious matter of concern in evaluating the performance of bio-resorbable drug eluting stents. As a result, today's chemical blends may be useful drug carrier materials for drug-loaded tube coatings capable delivering purgative drug in an incredibly tunable and regulated manner.

Simvastatin-Loaded Lipid Emulsion Nanoparticles: Characterizations and Applications.

Simvastatin (SIM) is a diet drug to treat high lipid levels in the blood. It has the drawback of being metabolized in humans' gastrointestinal tract (GIT) when taken in an oral dosage form. To enhance the role of SIM in treating hyperlipidemias and bypassing its metabolism in GIT, a biodegradable nanocarrier as a SIM-loaded lipid emulsion nanoparticle via the solvent injection method was designed. Cholesterol acts as a lipid core, and Tween 80 was utilized to stabilize the core. The optimized nanoformulation was characterized for its particle diameter, zeta potential, surface morphology, entrapment efficiency, crystallinity, and molecular interaction. Furthermore, the transdermal hydrogel was characterized by physical appearance, rheology, pH, and spreadability. In vitro assays were executed to gauge the potential of LENPs and olive oil for transdermal delivery. The mean particle size and zeta potential of the optimized nanoparticles were 174 nm and -22.5 mV 0.127, respectively. Crystallinity studies and Fourier transform infrared analyses revealed no molecular interactions. Hydrogels showed a sustained release compared to SIM-loaded LENPs that can be proposed as a better delivery system for SIM. We encourage further investigations to explore the effect of reported formulations for transdermal delivery by in vivo experiments.

Breast Cancer Detection and Classification Empowered With Transfer Learning.

Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.

Design and Evaluation of pH Sensitive PEG-Protamine Nanocomplex of Doxorubicin for Treatment of Breast Cancer.

Cancer is the most common cause of mortality worldwide. There is dire need of modern strategies-such as surface modification of nanocarriers-to combat this global illness. Incorporation of active targeting ligands has arisen as a novel platform for specific tumor targeting. The aim of the current study was to formulate PEG-protamine complex (PPC) of doxorubicin (DOX) for treatment of breast cancer (BC). DOX coupling with PEG can enhance cell-penetrating ability: combating resistance in MDA-MB 231 breast cancer cells. Ionic gelation method was adopted to fabricate a pH sensitive nanocomplex. The optimized nanoformulation was characterized for its particle diameter, zeta potential, surface morphology, entrapment efficiency, crystallinity, and molecular interaction. In vitro assay was executed to gauge the release potential of nanoformulation. The mean particle size, zeta potential, and polydispersity index (PDI) of the optimized nanoparticles were observed to be 212 nm, 15.2 mV, and 0.264, respectively. Crystallinity studies and Fourier transform infrared (FTIR) analysis revealed no molecular interaction and confirmed the amorphous nature of drug within nanoparticles. The in vitro release data indicate sustained drug release at pH 4.8, which is intracellular pH of breast cancer cells, as compared to the drug solution. PPC loaded with doxorubicin can be utilized as an alternative and effective approach for specific targeting of breast cancer.

Design and Evaluation of pH-Sensitive Nanoformulation of Bergenin Isolated from Bergenia ciliata.

The aim of the current study is extraction and isolation of bergenin from Bergenia ciliata and fabrication of pH-sensitive Eudragit® L100 (EL100) polymeric nanoparticles (NP) to tackle limitations of solubility. Bergenin-loaded EL100 nanoparticles (BN-NP) were fabricated via nanoprecipitation and an experimental design was conducted for optimization. A reverse phase-high performance liquid chromatography (RP-HPLC) method was developed for the quantitation of bergenin. The optimized nanoformulation was characterized by its particle size, morphology, loading capacity, entrapment efficiency, drug-excipient interaction and crystallinity. An in vitro assay was executed to gauge the release potential of pH-sensitive nanoformulation. The mean particle size, zeta potential and polydispersity index (PDI) of the optimized nanoparticles were observed to be 86.17 ± 2.1 nm, -32.33 ± 5.53 mV and 0.30 ± 0.03, respectively. The morphological analysis confirmed the spherical nature of the nanoparticles. Drug loading capacity and entrapment efficiency were calculated to be 16 ± 0.34% and 84 ± 1.3%, respectively. Fourier transform infrared spectroscopy (FTIR) studies unfolded that no interaction was present between the drug and the excipients in the nanoformulation. Crystallography studies revealed that the crystalline nature of bergenin was changed to amorphous and the nanoformulation was stable for up to 3 months at 40 °C. The present study confirms that bergenin isolation can be scaled up from abundantly growing B. ciliata. Moreover, it could also be delivered by entrapment in stimuli-responsive polymer, preventing the loss of drug in healthy tissues.

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning.

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

Hydrogel Containing Solid Lipid Nanoparticles Loaded with Argan Oil and Simvastatin: Preparation, In Vitro and Ex Vivo Assessment.

Transdermal hydrogels have the potential to improve therapeutic outcomes via enhancing bioavailability and reducing toxicity associated with oral delivery. The goal of the present study was to formulate and optimise argan oil loaded transdermal hydrogel containing lipid nanoparticles. The high pressure homogenization (HPH) method was utilised to fabricate Simvastatin loaded solid lipid nanoparticles (SIM-SLNs) with precirol ATO 5 as a lipid core and Poloxamer 407 (P407) to stabilise the core. The optimised nanoformulation was characterised for its particle diameter, zeta potential, surface morphology, entrapment efficiency, crystallinity and molecular interaction. Furthermore, transdermal hydrogel was characterised for physical appearance, rheology, pH, bio adhesion, extrudability, spreadability and safety profile. In vitro and ex vivo assays were executed to gauge the potential of SLNs and argan oil for transdermal delivery. The mean particle size, zeta potential and polydispersity index (PDI) of the optimised nanoparticles were 205 nm, -16.6 mV and 0.127, respectively. Crystallinity studies and Fourier transform infrared (FTIR) analysis revealed no molecular interaction. The in vitro release model explains anomalous non-Fickian release of drug from matrix system. Ex vivo skin penetration studies conducted through a fluorescence microscope confirmed penetration of the formulation across the stratum corneum. Hydrogel plays a crucial role in controlling the burst release and imparting the effect of argan oil as hypolipidemic agent and permeation enhancer.

Oral maxillofacial surgical residency and its impact on the academic and research productivity at different levels in Pakistan.

Oral and maxillofacial (OMF) surgery is a unique speciality. In many countries, OMFS is a dental speciality but the scope of its practice significantly overlaps with other specialities, including otolaryngology, head and neck surgery, plastic surgery, and orthopaedics. Thus, OMF surgery represents a true amalgamation of medical and dental specialities. There are different requirements of OMF residency training, which include a dental undergraduate training, medical training, or both. The training pathways for this speciality have evolved much in the last three decades and there is still no consensus over a single uniform path of becoming an OMF surgeon. An OMF surgeon deals with trauma, cysts, tumours and other pathologies of the maxilla, mandible, and zygomatic complex that need surgical correction. In addition to being a diverse speciality, the academic and research domains of residency have also changed. In Pakistan, residency training in OMF surgery started in 1994 and since then a lot of growth has taken place. This paper summarises the evolution and scope of OMF surgery and the contribution of this speciality in the overall academia and research in Pakistan's national dental scenario.

Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning.

Skin cancer is a major type of cancer with rapidly increasing victims all over the world. It is very much important to detect skin cancer in the early stages. Computer-developed diagnosis systems helped the physicians to diagnose disease, which allows appropriate treatment and increases the survival ratio of patients. In the proposed system, the classification problem of skin disease is tackled. An automated and reliable system for the classification of malignant and benign tumors is developed. In this system, a customized pretrained Deep Convolutional Neural Network (DCNN) is implemented. The pretrained AlexNet model is customized by replacing the last layers according to the proposed system problem. The softmax layer is modified according to binary classification detection. The proposed system model is well trained on malignant and benign tumors skin cancer dataset of 1920 images, where each class contains 960 images. After good training, the proposed system model is validated on 480 images, where the size of images of each class is 240. The proposed system model is analyzed using the following parameters: accuracy, sensitivity, specificity, Positive Predicted Values (PPV), Negative Predicted Value (NPV), False Positive Ratio (FPR), False Negative Ratio (FNR), Likelihood Ratio Positive (LRP), and Likelihood Ratio Negative (LRN). The accuracy achieved through the proposed system model is 87.1%, which is higher than traditional methods of classification.

Skin cancer biology and barriers to treatment: Recent applications of polymeric micro/nanostructures.

Background: Skin cancer has been the leading type of cancer worldwide. Melanoma and non-melanoma skin cancers are now the most common types of skin cancer that have been reached to epidemic proportion. Based on the rapid prevalence of skin cancers, and lack of efficient drug delivery systems, it is essential to surge the possible ways to prevent or cure the disease. Aim of review: Although surgical modalities and therapies have been made great progress in recent years, however, there is still an urgent need to alleviate its increased burden. Hence, understanding the precise pathophysiological signaling mechanisms and all other factors of such skin insults will be beneficial for the development of more efficient therapies. Key scientific concepts of review: In this review, we explained new understandings about onset and development of skin cancer and described its management via polymeric micro/nano carriers-based therapies, highlighting the current key bottlenecks and future prospective in this field. In therapeutic drug/gene delivery approaches, polymeric carriers-based system is the most promising strategy. This review discusses that how polymers have successfully been exploited for development of micro/nanosized systems for efficient delivery of anticancer genes and drugs overcoming all the barriers and limitations associated with available conventional therapies. In addition to drug/gene delivery, intelligent polymeric nanocarriers platforms have also been established for combination anticancer therapies including photodynamic and photothermal, and for theranostic applications. This portfolio of latest approaches could promote the blooming growth of research and their clinical availability.

Serious Inadequacies in High Alert Medication-Related Knowledge Among Pakistani Nurses: Findings of a Large, Multicenter, Cross-sectional Survey.

INTRODUCTION: Deaths-related to medications errors are common in Pakistan but these are not accurately reported. Recently, the death of a 9 months old baby due to abrupt administration of 15% potassium chloride injection sparked the issue of high alert medications (HAMs) related errors in the country. Since drug administration is the prime responsibility of the nurses, it is pivotal that they possess good knowledge of HAMs. Since there is no published data regarding the knowledge of HAMs among Pakistani nurses, we aimed to assess knowledge of HAMs among registered nurses of Pakistan. METHODS: A cross-sectional study was conducted among registered nurses, recruited using a convenient sampling technique, from 29 hospitals all over the Punjab Province. Data were collected using a validated self-administered instrument. All data were entered and analyzed using SPSS version 22. RESULTS: The study sample was comprised of 2,363 registered nurses (staff nurses = 94.8%, head nurses = 5.2%). Around 63% were working in tertiary hospitals whereas almost 25 and 12% were from district headquarter hospitals and tehsil headquarter hospitals, respectively. Around 84% of the study participants achieved scores <70%, indicating majority of Pakistani nurses having poor knowledge of HAMs administration as well as regulation. There was no significant difference of overall knowledge among age, hospitals, departments, training, designations, qualification, and experience categories. Major obstacles encountered during HAMs administration were "getting uncertain answers from colleagues" (72.9%), "unavailability of suitable person to consult" (61.1%) and "receiving verbal orders" (55.6%). CONCLUSION: Our study revealed the serious inadequacies in HAMs knowledge among Pakistani nurses which may lead to adverse patient outcomes. Nurses should receive comprehensive pharmacology knowledge not only during in-school nursing education but also as hospital-based continuing education. Moreover, it is of immense importance to bridge the gaps between physicians, clinical pharmacists, and nurses through effective communication as this will help reduce medication errors and improve patient care.

Recognizing Movement Disorder Emergencies - A Practical Review For Non-Neurologist.

Neurology still remains one of the most underserved specialties of medicine in Pakistan with roughly one neurologist per million people. Movement disorders (MD) are neurological problems that interfere with patient's motor abilities and diagnosis is typically clinical. In this review, we describe a practical approach to common MD emergencies that may be encountered by a non-neurologist physician, emphasizing on formulating a working diagnosis and their immediate management. Movement disorder emergencies can be classified based on MD phenomenology and we will provide a brief overview of dystonia including acute dystonic reaction, PAID syndrome and dystonic storm; chorea, myoclonus including serotonin syndrome and startle disease; and rigidity including neuroleptic malignant syndrome and malignant hyperthermia.

Evaluation of 13 rapidly mutating Y-STRs in endogamous Punjabi and Sindhi ethnic groups from Pakistan.

Y-chromosomal short tandem repeats (Y-STRs) are commonly used to study population histories, discover ancestral relationships, and identify males for criminal justice purposes. Y-STRs being largely in forensic use have low haplotype diversity in some populations and cannot discriminate between paternal male relatives. Rapidly mutating Y-STRs (RM Y-STRs) were breakthrough and have been paid much attention. A set of 13 rapidly mutating (RM) Y-STRs (DYF387S1, DYF399S1, DYF403S1a/b1/b2, DYF404S1, DYS449, DYS518, DYS526I/II, DYS547, DYS570, DYS576, DYS612, DYS626, and DYS627) typically reveals higher haplotype diversities than the commercially available Y-STR sets and allows differentiating male relatives for which commercial Y-STR sets are usually not informative. Here, we amplified the 13 RM Y-STRs in 168 (37 Sindhi and 131 Punjabi) individuals from Pakistani population, which is characterized by high rates of endogamy. The haplotype diversity and discrimination capacity were 1. Allelic frequencies ranged from 0.0060 to 0.5060, while gene diversity ranged from 0.6759 (DYS526a) to 0.9937 (DYF399S1). A total 319 different alleles were observed. Results of our study showed that RM Y-STRs provided substantially stronger discriminatory power in Pakistani populations.