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We investigated the impact of green tea powder (GTP) as an alternative to antibiotics on growth performance and gut health in broilers. A total of 340 one-day-old broilers were assigned to four dietary treatments as control (free of antibiotic), antibiotic, GTP 0.5 g/kg (GTP-1) and GTP 1 g/kg (GTP-2). Overall, the antibiotic group demonstrated significantly (P

This study introduces a novel and refined approach for generating exceptionally efficient S-boxes. The proposed methodology employs a hybrid approach that combines linear fractional transformation (LFT) with a multilayer perceptron (MLP) architecture. This method makes use of a perceptron with three layers: input, hidden, and output. Each layer's neuron count is fine-tuned to conform to the S-box layout. In addition, a threshold nonlinear transformation is utilized to increase nonlinearity, and a novel algorithm for boosting nonlinearity is introduced. The utilization of both LFT and MLP approaches has led to the development of S-boxes that possess nearly ideal average nonlinearity values, surpassing those that have been presented in literature. Notably, one S-box achieved an exceptional nonlinearity score of 114.50. Furthermore, to demonstrate how well the S-box works, this study also employs it in an image encryption application.

In recent times, the research community has shown interest in information security due to the increasing usage of internet-based mobile and web applications. This research presents a novel approach to constructing the nonlinear component or Substitution Box (S-box) of block ciphers by employing coset graphs over the Galois field. Cryptographic techniques are employed to enhance data security and address current security concerns and obstacles with ease. Nonlinear component is a keystone of cryptography that hides the association between plaintext and cipher-text. Cryptographic strength of nonlinear component is directly proportional to the data security provided by the cipher. This research aims to develop a novel approach for construction of dynamic S-boxes or nonlinear components by employing special linear group $ PSL(2, \mathbb{Z}) $ over the Galois Field $ GF\left({2}^{10}\right) $. The vertices of coset diagram belong to $ GF\left({2}^{10}\right) $ and can be expressed as powers of α, where α represents the root of an irreducible polynomial $ p\left(x\right) = {x}^{10}+{x}^{3}+1 $. We constructed several nonlinear components by using $ {GF}^{*}\left({2}^{10}\right) $. Furthermore, we have introduced an exceptionally effective algorithm for optimizing nonlinearity, which significantly enhances the cryptographic properties of the nonlinear component. This algorithm leverages advanced techniques to systematically search for and select optimal S-box designs that exhibit improved resistance against various cryptographic attacks.

Nowadays, various image encryption schemes based on chaotic systems have been developed, each of them has its own limitations and strength in terms of security and computational speed. The proposed image encryption scheme utilizes 2-D maps without disturbing their mathematical structure, characterized by topological features such as chaotic behavior and fractal properties, namely the Zaslavasky, Bakers, and Henon Maps. This approach utilizes both confusion and diffusion stages to achieve high levels of security against various attacks. The confusion stage utilizes chaotic values to muddle the rows and columns of the image, reducing the correlations between neighboring pixels, while the diffusion step achieves the avalanche effect with 2D Bakers map and Henon map. The proposed image encryption scheme is analyzed thoroughly to evaluate its security and performance. To evaluate the security and computational efficiency of the proposed image encryption method, various analysis such as correlation, contrast, entropy, energy, homogeneity, and performance analyses are conducted. Moreover, the three proposed S-boxes are also tested to evaluate their effectiveness using cryptographic analysis tests such as nonlinearity, strict Avalanche criterion, differential probability, linear probability, and bit independence criterion, which we also utilized in our proposed image encryption scheme.

Internet of vehicles (IoVs) allows millions of vehicles to be connected and share information for various purposes. The main applications of IoVs are traffic management, emergency messages delivery, E-health, traffic, and temperature monitoring. On the other hand, IoVs lack in location awareness and geographic distribution, which is critical for some IoVs applications such as smart traffic lights and information sharing in vehicles. To support these topographies, fog computing was proposed as an appealing and novel term, which was integrated with IoVs to extend storage, computation, and networking. Unfortunately, it is also challenged with various security and privacy hazards, which is a serious concern of smart cities. Therefore, we can formulate that Fog-assisted IoVs (Fa-IoVs), are challenged by security threats during information dissemination among mobile nodes. These security threats of Fa-IoVs are considered as anomalies which is a serious concern that needs to be addressed for smooth Fa-IoVs network communication. Here, smooth communication refers to less risk of important data loss, delay, communication overhead, etc. This research work aims to identify research gaps in the Fa-IoVs network and present a deep learning-based dynamic scheme named CAaDet (Convolutional autoencoder Aided anomaly detection) to detect anomalies. CAaDet exploits convolutional layers with a customized autoencoder for useful feature extraction and anomaly detection. Performance evaluation of the proposed scheme is done by using the F1-score metric where experiments are carried out by exploiting a benchmark dataset named NSL-KDD. CAaDet also observes the behavior of fog nodes and hidden neurons and selects the best match to reduce false alarms and improve F1-score. The proposed scheme achieved significant improvement over existing schemes for anomaly detection. Identified research gaps in Fa-IoVs can give future directions to researchers and attract more attention to this new era.

Abstract Vulnerabilities can have devastating effects on information security, affecting the economy, social stability, and national security. The idea of automatic vulnerability detection has always attracted researchers. From traditional manual vulnerability mining techniques to static and dynamic detection, all rely on human experts for feature definition. The rapid development of machine learning and deep learning has alleviated the tedious task of manually defining features by human experts while reducing the lack of objectivity caused by human subjective awareness. However, it is still necessary to find an objective characterisation method to define the features of vulnerabilities. Therefore, the authors use code metrics for code characterisation, sequences of metrics representing code. To use code metrics for vulnerability detection, a deep learning‐based vulnerability detection approach that uses a composite neural network of convolutional neural network (CNN) with long short‐term memory (LSTM) is proposed. The authors conduct experiments independently using the proposed approach for CNN‐LSTM CNN, LSTM, gated recurrent units (GRU), and deep neural network (DNN). The authors’ experimental results show that CNN‐LSTM has a high precision of 92%, a recall of 99%, and an accuracy of 91%. In terms of the F1‐score, it is 95%, compared to previous research results, which indicated an improvement of 18%. Compared to other deep learning‐based vulnerability detection models, the authors’ proposed model produced a lower false‐positive rate, a lower miss rate, and improved accuracy.

Background & objectives: Dengue virus (DENV) is an RNA virus that infects approximately 2.5 billion people around the world. The incidence of dengue fever has rapidly increased at an alarming rate in the last few years and has affected thousands of people in Pakistan. This review explores the prevalence, serotypes and pathogenesis of dengue virus circulating in Pakistan. Methods: A systematic review of observational studies published between 1994 and December 2019 was performed. All records of the confirmed outbreak of dengue fever in Pakistan were reviewed and articles containing no primary data were excluded. Results: Four identified serotypes of dengue virus (DENV 1-4) circulate in different regions of the world causing epidemics. The most prevalent serotype, which is still epidemic and dominant in Pakistan, is DENV-2. Many factors like over-population, rapid urbanization, travelling, lack of vector control in dengue endemic areas and inadequate health-care are responsible of dynamic and huge raise of dengue in Pakistan. Interpretation & conclusion: Currently there is no specific treatment for prevention of dengue virus. Recently some antiviral compounds were being tested to eradicate this disease. There is a need to develop an efficient and safe vaccine for all four serotypes to combat dengue viral infection globally and particularly in Pakistan.

Abstract Background In order to find a new natural resource for pain-relief, the analgesic effects of Ilex dipyrena crude extract, fractions, and subfractions were evaluated in in-vivo mouse models with possible mechanism of action. Methods Analgesic effects of crude extract (100 and 200 mg/kg body weight), fractions and subfractions (75 mg/kg body weight) were screened using heat-induced (tail-immersion and hot plate test) and chemical-induced (formalin and acetic acid) nociception models in mice. The samples were also tested for the elucidation of a possible mechanism through opioidergic and GABAergic systems. Results The administration of crude extract, fractions and subfractions produced analgesic responses in acetic acid, formalin, tail immersion, and hot plate model for pain similar to those obtained with the standard. Naloxone antagonized the antinociceptive effects of the tested samples, whereas bicuculline showed partial inhibition. Considering the analgesic response, crude extract, fractions, and subfractions demonstrated promising inhibitory activity against all test models for pain, which was further supported by the possible involvement of opioidergic and GABAergic systems. Conclusion The results suggest that this plant may be useful in the development of new analgesic drugs. Further research with regard to the isolation of bioactive compounds is required to verify these findings.

Background and ObjectiveViral hepatitis is a major public health concern on a global scale. It predominantly affects the world's least developed countries. The most endemic regions are resource constrained, with a low human development index. Chronic hepatitis can lead to cirrhosis, liver failure, cancer and eventually death. Early diagnosis and treatment of hepatitis infection can help to reduce disease burden and transmission to those at risk of infection or reinfection. Screening is critical for meeting the WHO's 2030 targets. Consequently, automated systems for the reliable prediction of hepatitis illness. When applied to the prediction of hepatitis using imbalanced datasets from testing, machine learning (ML) classifiers and known methodologies for encoding categorical data have demonstrated a wide range of unexpected results. Early research also made use of an artificial neural network to identify features without first gaining a thorough understanding of the sequence data.MethodsTo help in accurate binary classification of diagnosis (survivability or mortality) in patients with severe hepatitis, this paper suggests a deep learning-based decision support system (DSS) that makes use of bidirectional long/short-term memory (BiLSTM). Balanced data was utilized to predict hepatitis using the BiLSTM model.ResultsIn contrast to previous investigations, the trial results of this suggested model were encouraging: 95.08% accuracy, 94% precision, 93% recall, and a 93% F1-score.ConclusionsIn the field of hepatitis detection, the use of a BiLSTM model for classification is better than current methods by a significant margin in terms of improved accuracy.

In post disastrous situations, coordinated and integrated interventions aimed at relief and rehabilitation not only help facilitate reaching out to the affected communities in a timely fashion but also pave the way to channel scarce and valued resources towards end users in an efficient and effective manner. This article attempts to trace the origins and gradual development of ‘inter-agency collaboration’ and the implications thereof for disaster management strategies in Pakistan through an analysis of relief and rehabilitation interventions undertaken by the Government of Pakistan in collaboration with local and international Non-governmental Organisations (NGOs) and relief agencies in the ex post of the 2005 earthquake. Data for this study were collected through structured and semi-structured interviews from government officials, representatives of NGOs and relief agencies and ordinary women and men in the earthquake stricken localities of Balakot and Mansehra districts of Pakistan. On the heels of the 2005 earthquake, both local NGOs and faith-based organisations in concert with international NGOs and relief agencies from around the world rushed to assist Pakistan in it’s rescue and relief operations at a time when the country was faced with the twin dilemma of both the non-existence of peculiar institutional arrangements for disaster management and a lack of the necessary technical and financial resources. The aftermath of the 2005 earthquake offered opportunity to the Government of Pakistan and the NGOs and relief agencies alike to transform their individual interventions into a robust and organised ‘inter-agency collaboration’, which was later on realised in the form of establishment of a national disaster management organisation called the ‘Earthquake Reconstruction and Rehabilitation Authority (ERRA)’. The establishment of ERRA not only paved the way for avoiding duplication and wastage of resources but also ensued in reaching out to the affected communities in a timely fashion. The Pakistani case offers implications in terms of highlighting the salience of establishing ‘inter-agency collaboration’ in other settings.

Technology has played a vital part in improving quality of life, especially in healthcare. Artificial intelligence (AI) and the Internet of Things (IoT) are extensively employed to link accessible medical resources and deliver dependable and effective intelligent healthcare. Body wearable devices have garnered attention as powerful devices for healthcare applications, leading to various commercially available devices for multiple purposes, including individual healthcare, activity alerts, and fitness. The paper aims to cover all the advancements made in the wearable Medical Internet of Things (IoMT) for healthcare systems, which have been scrutinized from the perceptions of their efficacy in detecting, preventing, and monitoring diseases in healthcare. The latest healthcare issues are also included, such as COVID-19 and monkeypox. This paper thoroughly discusses all the directions proposed by the researchers to improve healthcare through wearable devices and artificial intelligence. The approaches adopted by the researchers to improve the overall accuracy, efficiency, and security of the healthcare system are discussed in detail. This paper also highlights all the constraints and opportunities of developing AI enabled IoT-based healthcare systems.

The recent state of the art innovations in technology enables the development of low-cost sensor nodes with processing and communication capabilities. The unique characteristics of these low-cost sensor nodes such as limited resources in terms of processing, memory, battery, and lack of tamper resistance hardware make them susceptible to clone node or node replication attack. The deployment of WSNs in the remote and harsh environment helps the adversary to capture the legitimate node and extract the stored credential information such as ID which can be easily re-programmed and replicated. Thus, the adversary would be able to control the whole network internally and carry out the same functions as that of the legitimate nodes. This is the main motivation of researchers to design enhanced detection protocols for clone attacks. Hence, in this paper, we have presented a systematic literature review of existing clone node detection schemes. We have also provided the theoretical and analytical survey of the existing centralized and distributed schemes for the detection of clone nodes in static WSNs with their drawbacks and challenges.

Medical imaging has become of vital importance for diagnosing diseases and conducting noninvasive procedures. Advances in eHealth applications are challenged by the fact that Digital Imaging and Communications in Medicine (DICOM) requires high-resolution images, thereby increasing their size and the associated computational complexity, particularly when these images are communicated over IP and wireless networks. Therefore, medical research requires an efficient coding technique to achieve high-quality and low-complexity images with error-resilient features. In this study, we propose an improved coding scheme that exploits the content features of encoded videos with low complexity combined with flexible macroblock ordering for error resilience. We identify the homogeneous region in which the search for optimal macroblock modes is early terminated. For non-homogeneous regions, the integration of smaller blocks is employed only if the vector difference is less than the threshold. Results confirm that the proposed technique achieves a considerable performance improvement compared with existing schemes in terms of reducing the computational complexity without compromising the bit-rate and peak signal-to-noise ratio.

Recently, linear wireless sensor networks (LWSNs) have been eliciting increasing attention because of their suitability for applications such as the protection of critical infrastructures. Most of these applications require LWSN to remain operational for a longer period. However, the non-replenishable limited battery power of sensor nodes does not allow them to meet these expectations. Therefore, a shorter network lifetime is one of the most prominent barriers in large-scale deployment of LWSN. Unlike most existing studies, in this paper, we analyze the impact of node placement and clustering on LWSN network lifetime. First, we categorize and classify existing node placement and clustering schemes for LWSN and introduce various topologies for disparate applications. Then, we highlight the peculiarities of LWSN applications and discuss their unique characteristics. Several application domains of LWSN are described. We present three node placement strategies (i.e., linear sequential, linear parallel, and grid) and various deployment methods such as random, uniform, decreasing distance, and triangular. Extensive simulation experiments are conducted to analyze the performance of the three state-of-the-art routing protocols in the context of node deployment strategies and methods. The experimental results demonstrate that the node deployment strategies and methods significantly affect LWSN lifetime.

Plant growth promoting rhizobacteria (PGPR) promote the plant growth by various direct and indirect mechanisms. The present study was undertaken to isolate and characterise the PGPRs of plum (Prunus domestica) rhizosphere in Pakistan. A total of 95 rhizobacteria were isolated, out of which 40 strains were selected on the basis of morphological, biochemical and Gram staining characteristics. The selected isolates were screened for in vitro plant growth promoting potential and were subsequently evaluated for host plant growth promotion. The selected isolates demonstrated strong lytic enzymatic activities and were able to produce ammonia, siderophore, Hydrogen cyanide along with capability of phosphate solubilisation. Moreover, the results showed a significant growth suppression of pathogenic Fusarium oxysporum and Rhizoctonia solani in an in vitro assay. The plant microbe interaction study was carried out using 11 most efficient rhizobacterial strains inoculated to roots of plum plants. The inoculated PGPRs significantly augmented the leaves number per shoot, shoot diameter, shoot length and plant height. The inoculation also significantly increased the chlorophyll contents of leaves, concentration of micro and macro nutrients compared with control. The current study shows the importance of these selected PGPRs as bio-fertilizer to improve the health and productivity of plum species in Pakistan.

Research efforts in the field of sentiment analysis have exponentially increased in the last few years due to its applicability in areas such as online product purchasing, marketing, and reputation management. Social media and online shopping sites have become a rich source of user-generated data. Manufacturing, sales, and marketing organizations are progressively turning their eyes to this source to get worldwide feedback on their activities and products. Millions of sentences in Urdu and Roman Urdu are posted daily on social sites, such as Facebook, Instagram, Snapchat, and Twitter. Disregarding people’s opinions in Urdu and Roman Urdu and considering only resource-rich English language leads to the vital loss of this vast amount of data. Our research focused on collecting research papers related to Urdu and Roman Urdu language and analyzing them in terms of preprocessing, feature extraction, and classification techniques. This paper contains a comprehensive study of research conducted on Roman Urdu and Urdu text for a product review. This study is divided into categories, such as collection of relevant corpora, data preprocessing, feature extraction, classification platforms and approaches, limitations, and future work. The comparison was made based on evaluating different research factors, such as corpus, lexicon, and opinions. Each reviewed paper was evaluated according to some provided benchmarks and categorized accordingly. Based on results obtained and the comparisons made, we suggested some helpful steps in a future study.

The coronavirus disease 19 (COVID-19) is a global pandemic of the twenty-first century and currently fourth wave is creating fear and panic worldwide. It is caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), a highly contagious viral infection of humans. The COVID-19 can be spread mainly through respiratory droplet particles and in contact with a COVID-19 infected person. Clinical manifestation of COVID-19 patients includes cough, fever, diarrhea, loss of taste and smell. In critical cases of COVID-19, the development of pneumonia and dyspnea leads to acute respiratory distress syndrome that may cause the death of the patient. It is well established that Angiotensin-Converting Enzyme 2 (ACE2) receptors on alveolar cells act as an entry gate for the SARS-COV-2. However, ACE2 is also highly expressed in multiple extrapulmonary vital organs such as the gastrointestinal system, cardiovascular system, kidney, etc. Therefore, the direct viral entry in these organs can be a likely pathway of injury. In addition, decoupling of immune responses leads to the cytokines storm, which might contribute to the injury of extrapulmonary organs. In this review, we report the multiple organ pathogenesis and clinical manifestations of COVID-19 patients, which could aid clinicians and researchers in prioritizing therapeutics remedies and developing research for all vital body systems involved.

Epidermal growth factor-like domain-8 (EGFL8) also known as vascular endothelial statin-2 (VE-statin-2). It was identified by using retroviral gene entrapment vectors, expressed in endothelial cells. It is located on chromosome6inhumansandchromosome17inthemouse. EGFL8codesaproteinof293aminoacidswithan amino-terminal signal peptide and has two EGF-like domains. EGFL8 plays a very important regulatory role in thymopoiesis, cell migration and invasion through the modulation of Notch signaling. Although the signaling regulatory factors of EGFL8 need to be explored but recent scientific advances have revealed some important aspect of its regulation. This review summarizes the current knowledge about all aspects of EGFL8 since its discovery.

Background Beaches are recreational spots for people. However, beach sand contains harmful microbes that affect human health, and there are no established methods for either sampling and identifying beach-borne pathogens or managing the quality of beach sand. Method This study was conducted with the aim of improving human safety at beaches and augmenting the quality of the beach experience. Beach sand was used as a resource to isolate bacteria due to its distinctive features and the biodiversity of the beach sand biota. A selected bacterial isolate termed FSRS was identified as Pseudomonas stutzeri using 16S rRNA sequencing and phylogenetic analysis, and the sequence was deposited in the NCBI GenBank database under the accession number MF599548. The isolated P. stutzeri bacterium was cultured in Luria–Bertani growth medium, and a crude extract was prepared using ethyl acetate to examine the potential pathogenic effect of P. stutzeri on human skin. A human skin keratinocyte cell line (HaCaT) was used to assess cell adhesion, cell viability, and cell proliferation using a morphological analysis and a WST-1 assay. Result The crude P. stutzeri extract inhibited cell adhesion and decreased cell viability in HaCaT cells. We concluded that the crude extract of P. stutzeri FSRS had a strong pathological effect on human skin cells. Discussion Beach visitors frequently get skin infections, but the exact cause of the infections is yet to be determined. The beach sand bacterium P. stutzeri may, therefore, be responsible for some of the dermatological problems experienced by people visiting the beach.

Objective: To evaluate the clinical presentation and outcome of Falciparum malaria in 100 hospitalized patients. Methodology: This hospital based descriptive study was conducted at Department of Medicine, Khyber Teaching Hospital, Peshawar from January 2011 to November 2011. One hundred patients of either gender, aged more than 12 years, diagnosed to have falciparum malaria on the basis of peripheral blood smear findings were included. Detailed information was recorded regarding presenting symptoms and signs of patients, response of patients to drugs used for Falciparum malaria, and complications of malaria. Results: Main presenting symptoms were fever (100%), headache (85%), chills (76%), nausea/vomiting (73%), altered level of consciousness (28%), fits (7%) and oliguria (5%). The main signs of Falciparum malaria were splenomegaly (75%), hepatomegaly (67%), anemia (60%), herpes labialis (42%) and jaundice (20%). Female patients were 42%, of whom 17% were pregnant. Ninety eight (98%) patients were treated with quinine + doxycycline, out of which seven (7%) patients died. Two (2%) patients were treated with combination of artemether and lumafantrine, both recovered completely. The common complications were anemia (60%), cerebral malaria (28%), hepatitis (20%), pulmonary edema (10%), hypoglycemia (8%), thrombocytopenia (7%), fundal hemorrhages (6%), renal failure (6%) and disseminated intravascular coagulation (5%). Conclusion: Falciparum malaria can present in a diverse spectrum. Fever with chills, headache, vomiting and altered level of consciousness are the common presenting symptoms. Anemia, cerebral malaria, hepatitis and pulmonary edema are the common complications of falciparum malaria. Response to quinine with doxycycline is good and mortality rate is 7%. Key Words: Falciparum Malaria, Quinine, Artemether, Artemisin-Based Combination Therapy, Complications.

Background: This study was aimed to determine the levels and ratios of serum LH and Testosterone, among men with history of infertility, having varying sperm counts. Material & Methods: Two hundred fifty married men, presenting with a complaint of infertility, who had been initially evaluated for their seminal profile, and had been classified into four (04) groups, as azoospermic (50), oligozoospermic (75), asthenozoospermic (50) and normozoospermic (75) were studied for the analysis of serum LH and Testosterone levels using Enzyme Immuno Assay (EIA), along with 50 proven fathers as a control group. The data was compared using student’s ‘t’ test. Results: LH indicated inverse/negative correlation to sperm concentration, while decreased Testosterone levels were associated with depleted sperm concentration. The mean levels of LH (mIU/ml) and Testosterone (nmol/L) for the groups were 13.85±2.33 and 11.86+0.70 (Azoospermia), 10.92±3.79 and 11.88+1.06 (Oligozoospermia), 3.92±1.17 and 16.24+2.05 (Asthenozoospermia), while the levels in normozoospermic men were 7.24±1.02 and 17.29+1.02, respectively. Similarly, the LH/T and T/LH ratios were 1.17(+/-0.28) and 0.86(+/-2.70) respectively in azoospermia, 0.92(+/-0.28)and 1.08(+/-0.17) in oligozoospermia, 0.30(+/-0.10)and 4.14(+/-10.4) in asthenozoospermia 0.42(+/-0.08) and 2.34(+/-0.48) in normozoospermic men. The proven fathers group had 7.74+0.71 mIU/ml LH and 15.88+ 1.15 nmol/ml testosterone, while the ratio of LH/T and T/LH was 0.49+ 0.28 and 2.05+ 0.33, respectively. Conclusion: The present data indicates that not only the disturbance in LH and Testosterone levels, but also disturbance in their ratios causes infertility, since these hormones act together by maintaining delicate feedback control system.

Objective: A community-based study was carried out on a random population sample from urban and rural areas of Bannu region, North West Frontier Province (NWFP) of Pakistan. The objective was to identify the genotypic frequency of blood groups in the region, in order to comprehend the allelic diversity. Material and Methods: Blood grouping was carried out over a period of 16 month from January 2002 to April 2003, and encompassed 2581 subjects, in which 57.09% were male and 42.09% female. These were categorized according to ABO/Rh system. Allele frequency was computed according to Hardy-Weinberg law. Results: The distribution of phenotypes in the total sample were 0.3623, 0.3103, 0.2507 and 0.0767 for group B, A, O and AB, respectively, with 0.672 Rh positive ® and 0.328 Rh negative (r). B group was dominant in both the genders, and AB was rare in both males as well as females. The distribution of the alleles in the total sample was 0.345, 0.378 and 0.277 for IA, IB and i, respectively. Conclusion: The studied population exhibited a predominance of group B, in the order of B>A>O>AB, as well as Rh positive antigen for both male (90.26%) and female (87.98%) subjects within the population, with Rh negative men and women being 9.74% and 12.02% respectively. Allele frequency recorded was in the order of IB>IA>i, and R>r.

Background: To observe the variation in semen quality of infertile men, with the purpose of generating preliminary data in our population. Material & Methods: Analysis of semen including volume, liquefaction time, pH, sperm concentration and quality was carried out over a period of 14 months from November 2003 to December 2004. A total of 350 men presenting as infertile couples were studied. Semen analysis was performed in Department of Reproductive Physiology/Health, Public Health Laboratories Division, National Institute of Health, Islamabad, as a part of fertility assessment. Results: According to the sperm concentration, the samples were categorized in four different groups; (A) Azoospermic group having absence of sperms, (B) Oligozoospermic group with a sperm concentration of less than 20 million/ml, (C) Asthenozoospermic group with a sperm concentration greater than 20 million/ml but with less than 25% of progressive motility and (D) Normozoospermic group with a sperm concentration of more than 20 million/ml and sperm motility of 60% or greater, with more than 25% progressive motility. Linear regression analysis shows a decrease in semen volume in group A and B. The mean liquefaction time showing linear increase in group A and B. The pH did not vary too much among all the groups. The mean of sperm concentration was 0, 6.7+1.7, 45.3+ 8.8 and 86.8 + 7.5 million/ml in groups A, B, C and D respectively. Conclusion: Semen analysis provides important information about the quality and quantity of the sperm and is an integral part of infertility work up.