Cancer is one of the major causes of mortality and morbidity in the world. Pain is the most debilitating and exhausting symptom of the cancer. It also has deep and intense impact on patient’s quality of life. In advanced stages of cancer, the incidence of pain approaches up to 70–80%. Cancer pain can be effectively treated by expert hands. Strong opioids are the mainstay in the WHO analgesic ladder, specifically for cancer pain patients. Unfortunately, in most of the developing countries patients with cancer pain remains under-treated because of the non-availability of strong opioids. This situation is a real challenge for a pain physician. Regardless of all the knowledge and skill, provision of effective pain relief becomes an uphill task. This editorial is an attempt to highlight the plight of the cancer pain patients and the frustration of the treating physicians. We need to strengthen and upgrade our policies and protocols to provide comfort to cancer patients. Key words: Cancer pain; Pain Management; WHO Guidelines; Opioids. Citation: Mushtaq S, Waheed H, Ghafoor AUR, Bashir K. The misery of cancer pain. Anaesth. pain intensive care 2022;27(1):03−05; DOI: 10.35975/apic.v27i1.2130 Received: December 13, 2022; Accepted: December 24, 2022

OBJECTIVE:To assess the prevalence of chronic pain, its physical and psychosocial impact on daily life, and the various therapies adopted to alleviate pain.METHODS:The cross-sectional population-based telephonic survey was conducted from May to July 2021 at Shaukat Khanum Memorial Cancer Hospital, Lahore, Pakistan, and comprised patients of either gender aged at least 18 years suffering from chronic pain who visited the institutional laboratory collection centres. In the first phase, people who were suffering from chronic pain were screened, while in the second phase, data was collected using a detailed questionnaire exploring pain history, treatment and its effects. The data was compiled and analysed using Antlere's AI based software.RESULTS:Of the 4,801 patients contacted, 757(15.75%) were suffering from chronic pain. A total of 201(20%) subjects reported that their pain score was 5/10 on the numerical rating scale. Back pain was the major complaint (183, 18%) among the subjects. Of the total, 335(44.25%) were having active treatment, and 226 (67%) of them said the medication was effective. Overall, 706 (93%) patients had never visited a pain management specialist. Furthermore, 252 (33%) participants were diagnosed with depression, and 106 (14%) patients said that they were suicidal at some point in life.CONCLUSIONS:.The survey observed that a high percentage of unawareness existed on pain management among the Pakistani citizens.

Objective: To determine the frequency of successful cannulation of ultrasound assisted inline oblique transducer approach for internal jugular venous cannulation. Study Design: Descriptive Case Series. Setting: Department of Anesthesia and Intensive Care, Hameed Latif Hospital, Lahore. Period: 22-02-2017 to 22-08-2017. Material & Methods: In this study the cases were included of both gender and age between 18 to 65 years. Jugular vein was visualized ultrasonographically in an oblique axis and the needle was inserted in the same plane, aligned with the longitudinal axis of the transducer. Success rate was noted. Results: The mean age of patients was 49.94±10.90 years, male to female ratio of the patients was 1.9:1. In this study the successful cannulation was observed in 145/155 (93.55%) patients. Conclusion: It has been observed in this study that the ultrasound assisted inline oblique transducer approach is successful technique for IJV cannulation.

Software defined WiFi network (SD-WiFi) is a new paradigm that addresses issues such as mobility management, load management, route policies, link discovery, and access selection in traditional WiFi networks. Due to the rapid growth of wireless devices, uneven load distribution among the network resources still remains a challenging issue in SD-WiFi. In this paper, we design a novel four-tier software defined WiFi edge architecture (FT-SDWE) to manage load imbalance through an improved handover mechanism, enhanced authentication technique, and upgraded migration approach. In the first tier, the handover mechanism is improved by using a simple AND operator and by shifting the association control to WAPs. Unauthorized user load is mitigated in the second tier, with the help of base stations (BSs) which act as edge nodes (ENs), using elliptic ElGamal digital signature algorithm (EEDSA). In the third tier, the load is balanced in the data plane among the OpenFlow enabled switches by using the whale optimization algorithm (WOA). Moreover, the load in the fourth tier is balanced among the multiple controllers. The global controller (GC) predicts the load states of local controllers (LCs) from the Markov chain model (MCM) and allocates packets to LCs for processing through a binary search tree (BST). The performance evaluation of FT-SDWE is demonstrated using extensive OMNeT++ simulations. The proposed framework shows effectiveness in terms of bandwidth, jitter, response time, throughput, and migration time in comparison to SD-WiFi, EASM, GAME-SM, and load information strategy schemes.

Some things come easily to humans, one of them is the ability to navigate around.This capability of navigation suffers significantly in case of partial or complete blindness, restricting life activity.Advances in the technological landscape have given way to new solutions aiding navigation for the visually impaired.In this paper, we analyze the existing works and identify the challenges of path selection, context awareness, obstacle detection/identification and integration of visual and nonvisual information associated with real-time assisted mobility.In the process, we explore machine learning approaches for robotic path planning, multi constrained optimal path computation and sensor based wearable assistive devices for the visually impaired.It is observed that the solution to problem is complex and computationally intensive and significant effort is required towards the development of richer and comfortable paths for safe and smooth navigation of visually impaired people.We cannot overlook to explore more effective strategies of acquiring surrounding information towards autonomous mobility.

Comfortable movement of a visually impaired person in an unknown environment is non-trivial task due to complete or partial short-sightedness, absence of relevant information and unavailability of assistance from a non-impaired person.To fulfill the visual needs of an impaired person towards autonomous navigation, we utilize the concepts of graph mining and computer vision to produce a viable path guidance solution.We present an architectural perspective and a prototype implementation to determine safe & interesting path (SIP) from an arbitrary source to desired destination with intermediate way points, and guide visually impaired person through voice commands on that path.We also identify and highlight various challenging issues, that came up while developing a prototype solution, i.e.VIPEye -An Eye for Visually Impaired People, to this aforementioned problem, in terms of task's difficulty and availability of required resources or information.Moreover, this study provides candidate research directions for researchers, developers, and practitioners in the development of autonomous mobility services for visually impaired people.

Objective: To determine the sociodemographic correlates and pattern of psychiatric disorders in patients admitted at Armed Forces Institute of Mental Health (AFIMH). Study Design: Hospital based cross sectional study. Place and Duration of Study: This study was conducted at the Armed Forces Institute of Mental Health (AFIMH) Rawalpindi, from Apr 2015 to Jul 2015. Methodology: All patients meeting the inclusion criteria admitted during the study period were enrolled after their informed consent. Psychiatric interview was carried out using the Present State Examination (PSE) and diagnosis was made according to the ICD 10 criteria. Results: Out of 190 enrolled subjects, 148 (77.9%) were male with mean age ± SD of 32.96 ± 10.87 years while 42 (22.1%) were females with mean age ± SD of 31.19 ± 12.13 years. Post stratification chi square test revealed that age was an effect modifier for bipolar affective disorder (p-value 0.004) and substance use disorder (p-value 0.04). Gender was an effect modifier for depression (p-value 0.001), dissociative disorder (p-value 0.005) and substance use disorder (p-value 0.0001). Marital status was an effect modifier for depression only. The most common psychiatric diagnosis was bipolar affective disorder (38.9%) followed by depression (23.2%) and substance use disorder (20%). Other diagnosis included schizophrenia (7.4%), adjustment disorder (5.8%), dissociative disorders (4.2%) and PTSD (0.5%). Conclusion: Younger age was positively associated with bipolar affective disorder and substance use disorder. Depression and dissociative disorders were positively associated with female gender where as substance use disorder was associated with the male gender.

Gait is a unique non-invasive biometric form that can be utilized to effectively recognize persons, even when they prove to be uncooperative. Computer-aided gait recognition systems usually use image sequences without considering covariates like clothing and possessions of carrier bags whilst on the move. Similarly, in gait recognition, there may exist unknown covariate conditions that may affect the training and testing conditions for a given individual. Consequently, common techniques for gait recognition and measurement require a degree of intervention leading to the introduction of unknown covariate conditions, and hence this significantly limits the practical use of the present gait recognition and analysis systems. To overcome these key issues, we propose a method of gait analysis accounting for both known and unknown covariate conditions. For this purpose, we propose two methods, i.e., a Convolutional Neural Network (CNN) based gait recognition and a discriminative features-based classification method for unknown covariate conditions. The first method can handle known covariate conditions efficiently while the second method focuses on identifying and selecting unique covariate invariant features from the gallery and probe sequences. The feature set utilized here includes Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and Haralick texture features. Furthermore, we utilize the Fisher Linear Discriminant Analysis for dimensionality reduction and selecting the most discriminant features. Three classifiers, namely Random Forest, Support Vector Machine (SVM), and Multilayer Perceptron are used for gait recognition under strict unknown covariate conditions. We evaluated our results using CASIA and OUR-ISIR datasets for both clothing and speed variations. As a result, we report that on average we obtain an accuracy of 90.32% for the CASIA dataset with unknown covariates and similarly performed excellently on the ISIR dataset. Therefore, our proposed method outperforms existing methods for gait recognition under known and unknown covariate conditions.

Building autonomous and intelligent robots has been an elusive dream for researchers for some time. Simultaneous Localization and Mapping (SLAM) systems have contributed towards achieving this goal by making robots better in navigating through complex environments. Until now it has only been possible to train and teach robots to move around in particular environments using a certain set of rules and heuristics. With the sudden surge in interest in AI and Machine Learning, a lot of effort has been put in into making robots intelligent and for them to automatically learn their paths in unknown environments (also referred to as Path Planning). This however has been met with mixed results as either the solution proposed is not too practical (e.g. requires too much training) or has limited success (e.g. works in specific environments). In this research, we develop a novel autonomous path planning framework using Deep Learning which can learn to navigate in unknown environments. The system has been tested on state-of-the-art Active Vision Dataset with promising results.

The recent development in the technology has increased the complexity of image contents and demand for image classification becomes more imperative. Digital images play a vital role in many applied domains such as remote sensing, scene analysis, medical care, textile industry and crime investigation. Feature extraction and image representation is considered as an important step in scene analysis as it affects the image classification performance. Automatic classification of images is an open research problem for image analysis and pattern recognition applications. The Bag-of-Features (BoF) model is commonly used to solve image classification, object recognition and other computer vision-based problems. In BoF model, the final feature vector representation of an image contains no information about the co-occurrence of features in the 2D image space. This is considered as a limitation, as the spatial arrangement among visual words in image space contains the information that is beneficial for image representation and learning of classification model. To deal with this, researchers have proposed different image representations. Among these, the division of image-space into different geometric sub-regions for the extraction of histogram for BoF model is considered as a notable contribution for the extraction of spatial clues. Keeping this in view, we aim to explore a Hybrid Geometric Spatial Image Representation (HGSIR) that is based on the combination of histograms computed over the rectangular, triangular and circular regions of the image. Five standard image datasets are used to evaluate the performance of the proposed research. The quantitative analysis demonstrates that the proposed research outperforms the state-of-art research in terms of classification accuracy.

Classification and analysis of Arabic text in terms of social media analysis is presented here in this work. This work, keeping in view the morphological and syntactical difficulties in Arabic language, focuses on negative and positive sentiment candidates for polarity as a first step towards a major goal of building a complete framework for political sentiment analysis. There are many works for sentiment classification and analysis in literature, which can be categorized into supervised, unsupervised, and hybrid. However, majority of these methods either lack in availability of sufficient size dataset for learning or suffer from Arabic language complications in terms of processing diverse nature of text. To overcome this issue, we introduce a semi-supervised approach for sentiment classification and analysis in Arabic language. Our approach is based on the concept of word embedding model to improve the performance of classification even with small size seed data. It has the capability to model the words in a large vector space where similar words are expected to occur in close proximity. Once the data is mapped to vector space model, then we utilize various classifiers to learn the patterns in the lexicon and predict the classification as positive or negative for unknown similar words. Classifiers such as Stochastic Gradient Descent and SVM are trained and tested with the specified 80% and 20% data ratio. Our approach yields around 80% accuracy through intermediate experiments. It has been observed that besides the common problem of lexicon size, this is attributed mainly to the quality of word embedding available for the Arabic language. Index Terms Sentiment Analysis, Classification, Tweets, Polarity, Arabic Language, Lexicon, Text Processing

Background The supply of controlled drugs is limited in the Far East, despite the prevalence of health disorders that warrant their prescription. Reasons for this include strict regulatory frameworks, limited financial resources, lack of appropriate training amongst the medical profession and fear of addiction in both general practitioners and the wider population. Consequently, the weak opioid tramadol has become the analgesic most frequently used in the region to treat moderate to severe pain. Methods To obtain a clearer picture of the current role and clinical use of tramadol in Southeast Asia, pain specialists from 7 countries in the region were invited to participate in a survey, using a questionnaire to gather information about their individual use and experience of this analgesic. Results Fifteen completed questionnaires were returned and the responses analyzed. Tramadol is used to manage acute and chronic pain caused by a wide range of conditions. Almost all the specialists treat moderate cancer pain with tramadol, and every one considers it to be significant or highly significant in the treatment of moderate to severe non-cancer pain. The reasons for choosing tramadol include efficacy, safety and tolerability, ready availability, reasonable cost, multiple formulations and patient compliance. Its safety profile makes tramadol particularly appropriate for use in elderly patients, outpatients, and for long-term treatment. The respondents strongly agreed that tighter regulation of tramadol would reduce its medical availability and adversely affect the quality of pain management. In some countries, there would no longer be any appropriate medication for cancer pain or the long-term treatment of chronic pain. Conclusions In Southeast Asia, tramadol plays an important part in the pharmacological management of moderate to severe pain, and may be the only available treatment option. If it were to become a controlled substance, the standard of pain management in the region would decline.

Human activity recognition (HAR) has significance in the domain of pattern recognition. HAR handles the complexity of human physical changes and heterogeneous formats of same human actions performed under dissimilar subjects. This research contributes a unique method focusing on the changes in human movement. The purpose is to identify and categorise human actions from video sequences. The interest points (IPs) are extracted from the subject video and motion history images (MHIs) are constructed and analysed after image segmentation. Discriminative features (DFs) are selected and the visual vocabulary is learned from the extracted DF (EDF). The EDF are then quantised by using visual vocabulary and images are represented based upon frequencies of visual words (VW). VW are formed from the EDF and then, a histogram of VW is developed based on the feature vectors extracted from MHI. These feature vectors are used for training support vector machine (SVM) for the classification of actions into various categories. Benchmark datasets like KTH, Weizmann and HMDB51 are used for evaluation and comparison with existing action recognition approaches depicts the better performance of adopted strategy.

Objectives: To compare the psychiatric morbidity in acclimatized “deployed” troops with acclimatized but “not yet deployed” troops and to find out the usefulness of General Health Questionnaire – 12, as a screening tool to identify psychiatric morbidity in troops at high altitudes. Study Design: Comparative study. Duration and Place: The study was conducted at Siachen from June to July 1996. Patients and Methods: The study population (n= 245) was divided into two groups. Group I (n=126) comprised of troops acclimatized and trained for 07 weeks below 14000 feet by staged and graded ascent but were “not yet deployed”, and Group II (n=119) comprised of acclimatized troops who remained “deployed” above 15000 feet for an average duration of 07 weeks, and had descended to a mean height of 14200 feet, in previous two - three weeks. General Health Questionnaire – 12 and Present State Examination were used for psychiatric evaluation. Results: Out of 245 troops exposed to high altitude, 105 (42.8%), had psychiatric morbidity, as measured by a score of more than 2 on General Health Questionnaire -12 and a positive International Classification of Diseases – 10 diagnosed on clinical psychiatric interview based on Present State Examination. More troops 67 (56.3%), in Group II had psychiatric morbidity as compared to Group I, 38 (30.2%). Psychotic symptoms; delusions 2 (1.68%) and hallucinations 3 (2.52%) were seen in Group II patients whereas no psychotic symptoms were seen in Group I. The psychotic symptoms resolved completely after descending to 14200 feet but the neurotic symptoms, did not resolve completely. Cases, scoring 2/12 or above (42.8%) on General Health Questionnaire -12 were highly associated with a positive psychiatric diagnosis. The sensitivity, specificity and Positive Predictive Value and Negative Predictive Value of GHQ-12 in group I was 100%, 91%, 82.6% and 100%; whereas in group II it was 100%, 98.1%, 98.5% and 100% respectively. Conclusion: High altitude “deployment” is stressful for troops and is associated with development of “psychotic and “neurotic” symptoms above 15000 feet. The “psychotic” symptoms abate completely but some “neurotic” symptoms persist even after descent to 14200 feet. General Health Questionnaire – 12 followed by psychiatric interview can effectively determine psychiatric morbidity in troops deployed at high altitudes.

Terrorism and violence are used by miscreant groups and individuals to disrupt the normal course of events. While not a new phenomenon, the information age offers new and innovative methods for spreading messages related to terrorism and expansion through recruitment on social media. These observations are alarming due to the broad reach and speed of propagation made available by social media. To ensure safety, harmony, and peace, it is important that the use of social media for terrorism is minimised. We discuss the various methods used by terrorists on social media to increase exposure and identify how the inherent structure of social media, the amount of data available, and language understanding pose challenges as well as opportunities for control efforts. We propose a strategy for restraining terrorist activities through data mining methods on big data created by social media in combination with natural language processing for language understanding and social network analysis for uncovering the underlying structure and association of terrorist groups and their activities

Background and Objectives: Patient identification with difficult intubation is important in planning anesthetic management and one major factor for difficult intubation in t he obese patients is large neck circumference. The need for prediction of a potentially difficult airway received great importance as it plays a significant role in reducing morbidity and mortality. Therefore, this study was done to glimpse the effect of neck circumference on endotracheal intubation and to determine the frequency of difficult intubation.Material and Methods: The study was cross sectional descriptive study and convenient sampling technique was used. Seventy patients of age between 19-50 years of both sexes were enrolled. Neck circumference was measured at the level of cricoid cartilage along with other airway assessments. Direct laryngoscopy was done and checked whether it is difficult one or easy using Intubation difficulty scale. Data were entered and analyzed by using statistical software SPSS version 15.0. Results: Mean BMI was noted as 33.02±2.30 kg/m2 and the mean neck circumference was 43.64±2.30 cm. Difficult intubation was observed in 23 (32.86%) patients with mean neck circumference of 45.44±1.88 cm and normal intubation observed in 47(67.14%) patients with mean neck circumference of 42.77±1.98 cm. Linear correlation was found between the neck circumference and Intubation Difficulty Scale score with value of Pearson correlation=0.617 .Conclusion: Neck circumference of patient was found to have significant effects on difficult intubation. Frequency of difficult intubation was found in almost one third of obese patients with increasing neck circumference.Janaki Medical College Journal of Medical Sciences (2016) Vol. 4 (2): 3-9

Audio steganography has attracted a great attention owing to secure communication for commercial and military purposes due to larger size of their files. Among these, audio based steganography has more potential to conceal information because audio files are larger in size as compare to other media. For audio steganography, mostly Least Significant Bit (LSB) techniques are used to hide the secrete data. LSB techniques hide data in limited capacity. Also, detection of data in lower bits is much easier, thus these techniques has less security. In this paper, audio based steganography technique is suggested. The proposed technique conceals data at the random location of audio signal by using chaotic based technique. The proposed technique enables us to hide the data in lower as well as higher significant bits of the audio data; that increases its capacity. It also enhances the security of the secrete data by placing at random location of the audio data. It believes that the given technique can be useful for secure communication of large amount of secrete data.

This study proposes an automatic method for key events detection and summarization for cricket videos, particularly because of the longest match durations, broadcasting time concerns and largely available multimedia content. In the proposed work, first rule-based induction is applied to detect excited audio clips in cricket videos, and then a decision tree framework is designed for video summarization. The proposed method evaluated on a diverse dataset with average accuracy of 95% signifies the effectiveness in terms of video summarization. Hence, the cricket videos can reliably be broadcasted over the low bandwidth networks and transmission with time constraints.

Objective: To determine the frequency of Von Willebrand disease (vWD) in patients of heavy menstrual bleeding (HMB). Study Design: Hospital based cross sectional study. Place and Duration of Study: Study was conducted at the Gynecology and Obstetrics department, Military Hospital, Rawalpindi in collaboration with Haematology Department of Armed Forces Institute of Pathology (AFIP) Rawalpindi, from Jul to Dec 2015. Material and Methods: Women presenting with HMB were enrolled in the study after informed consent. HMB was defined as cyclical bleeding at normal intervals but patient is using more than 5 pads per day or increase in duration 8/28 or more for at least last 06 months. Venous blood samples were taken and screened for the hemoglobin level (Hb), platelet count, prothrombin time (PT), activated partial thromboplastin time (aPTT) and Von Willebrand antigen (vWF:Ag) in addition to bleeding time (BT) at the Armed Forces Institute of Pathology (AFIP). The demographic details (age, age at menarche), clinical features (menstrual history, quantity of bleeding) and laboratory findings were recorded on the study proforma. Results: A total of 200 patients were enrolled in this study with mean age of 32.3 ± 8.5 years. Mean flow of menstrual blood was 9.8 ± 2.5 pads / day. Mean Hb% was 8.1 ± 1.4 g/dl. Twenty nine (14.5%) patients were having low level of vWF:Ag. Conclusion: There is high frequency of von Willebrand disease among females presenting with heavy menstrual bleeding in our set up. Therefore all patients with heavy menstrual bleeding except those with obvious causes like multiple fibroid should be screened for von Willebrand disease.