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  1. Article: Rate of various access sites for temporary transvenous pacing and different outcomes at Lady Reading Hospital, Peshawar Pakistan.

    Adil, Muhammad / Khan, Sher Bahadar / Khan, Muhammad Shahbaz / Hassan, Zair

    Pakistan journal of medical sciences

    2023  Volume 39, Issue 4, Page(s) 1101–1107

    Abstract: Objective: To evaluate the various temporary transvenous pacemaker (TPM) access sites, its indications, procedural complications, and outcomes of patients.: Methods: This prospective study conducted in a tertiary care hospital of Peshawar, included ... ...

    Abstract Objective: To evaluate the various temporary transvenous pacemaker (TPM) access sites, its indications, procedural complications, and outcomes of patients.
    Methods: This prospective study conducted in a tertiary care hospital of Peshawar, included 100 patients, who underwent TPM for any reasons, via the trans jugular, subclavian, or trans-femoral route. The duration of the study was from October 1
    Results: Of the 100 patients who underwent temporary transvenous pacing, 56%were males and 44% were females, with an age range of 46-80 years. In majority of the patients, (N =54) internal jugular vein was used as the venous access site followed by the subclavian vein. (N=24). Coronary artery disease was prevalent in 42% of the patients. 50% had complete AV block, 19% had symptomatic second-degree block, and 10% had sinus nodal diseases. Seventy three percent of the patients needed TPM implantation on an emergency basis, which is statistically significant (p=0.009). Almost 40% of the patient ultimately underwent a permanent pacemaker. Out of 100 patients, 16 patients expired. The major procedure related complications were bleeding 16% overall at the puncture site and 14.8% in the internal jugular group. Other complications were local infection 13% at the insertion site followed by hemopericardium 3%, in the internal jugular group.
    Conclusion: Atrioventricular block is the commonest indication for temporary pacing in our study. The average time the TPM remained in place was significantly higher in the trans jugular approach group along with a higher complication rate in this group.
    Language English
    Publishing date 2023-08-08
    Publishing country Pakistan
    Document type Journal Article
    ZDB-ID 2032827-8
    ISSN 1681-715X ; 1682-024X ; 1017-4699
    ISSN (online) 1681-715X
    ISSN 1682-024X ; 1017-4699
    DOI 10.12669/pjms.39.4.7467
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3948: Show issues Location:
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  2. Book ; Online: RNA-TransCrypt

    Khan, Muhammad Shahbaz / Ahmad, Jawad / Al-Dubai, Ahmed / Ghaleb, Baraq / Pitropakis, Nikolaos / Buchanan, William J.

    Image Encryption Using Chaotic RNA Encoding, Novel Transformative Substitution, and Tailored Cryptographic Operations

    2024  

    Abstract: Given the security concerns of Internet of Things (IoT) networks and limited computational resources of IoT devices, this paper presents RNA-TransCrypt, a novel image encryption scheme that is not only highly secure but also efficient and lightweight. ... ...

    Abstract Given the security concerns of Internet of Things (IoT) networks and limited computational resources of IoT devices, this paper presents RNA-TransCrypt, a novel image encryption scheme that is not only highly secure but also efficient and lightweight. RNA-TransCrypt integrates the biocryptographic properties of RNA encoding with the non-linearity and unpredictability of chaos theory. This scheme introduces three novel contributions: 1) the two-base RNA encoding method, which transforms the image into RNA strands-like sequence, ensuring efficient scrambling; 2) the transformative substitution technique, which transforms the s-box values before replacing the pixel values, and is responsible for making the scheme lightweight; and 3) three mathematical cryptographic operations designed especially for image encryption that ensure the effective transformation of the s-box values, resulting in a new outcome even for the same input values. These modules are key-dependent, utilizing chaotic keys generated by the De Jong Fractal Map and the Van der Pol Oscillator. Extensive security analysis, including histogram analysis, correlation analysis, and the results of the statistical security parameters obtained from the Gray-Level Co-occurrence Matrix (GLCM) validate the efficacy of the proposed scheme in encrypting input images with close-to-ideal results of 7.997 entropy and 0.0006 correlation.
    Keywords Computer Science - Cryptography and Security
    Subject code 005
    Publishing date 2024-01-09
    Publishing country us
    Document type Book ; Online
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  3. Article ; Online: COVID19: A Matter of State and Faith.

    Siraj, Abuzar / Hassan, Zair / Khan, Muhammad Shahbaz

    Journal of the College of Physicians and Surgeons--Pakistan : JCPSP

    2020  Volume 30, Issue 6, Page(s) 81

    MeSH term(s) Asymptomatic Diseases ; Betacoronavirus ; COVID-19 ; Coronavirus ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; Coronavirus Infections/transmission ; Humans ; Pandemics/prevention & control ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/prevention & control ; Pneumonia, Viral/transmission ; Public Health ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-07-28
    Publishing country Pakistan
    Document type Journal Article
    ZDB-ID 2276646-7
    ISSN 1681-7168 ; 1022-386X
    ISSN (online) 1681-7168
    ISSN 1022-386X
    DOI 10.29271/jcpsp.2020.Supp1.S81
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Social determinants of pacemaker reuse among patients and family members in Pakistan.

    Mustafa, Bilal / Butt, Hamza / Khan, Muhammad Shahbaz / Rashid, Sarim / Noor, Tayyiba Ahmed / Alam, Shafiq / Ashraf, Waheed / Malik, Jahanzeb

    Expert review of cardiovascular therapy

    2023  Volume 21, Issue 2, Page(s) 145–150

    Abstract: Objectives: This survey aimed to quantify the opinions of CIED reuse among patients and family members in Pakistan and to identify the social determinants which may predict these views.: Methods: A questionnaire formulating attitudes toward PPM reuse ...

    Abstract Objectives: This survey aimed to quantify the opinions of CIED reuse among patients and family members in Pakistan and to identify the social determinants which may predict these views.
    Methods: A questionnaire formulating attitudes toward PPM reuse was administered to patients and family members at cardiology institutes in Pakistan from 1 July 2022 to 30 September 2022. The eligibility criteria (age > 18 years; inline for PPM placement) were taken into account and incomplete responses were excluded from the final analysis.
    Results: A total of 9,246 participants recorded their responses, of which 7,152 (78.16%) accepted pre-used PPMs. The lower social class had more PPM reuse acceptance rate than the middle and upper class (92.72% vs. 60.52% vs. 35.38%), respectively. Age ≥ 65 (OR(95%CI): 0.68 (0.41-0.99); P-value = 0.023), male gender (OR(95%CI): 0.55 (0.35-0.72), P-value = 0.016), unemployment (OR(95%CI): 0.47 (0.25-0.64); P-value = 0.007), poor health status (OR(95%CI): 0.72 (0.53-0.92); P-value = 0.041), and lower social class (OR(95%CI): 0.36 (0.28-0.53); P-value = 0.003) were social determinants of PPM reuse acceptance.
    Conclusion: Patients and their family members endorse the concept of PPM reuse in Pakistan who cannot afford new devices.
    MeSH term(s) Humans ; Male ; Adult ; Middle Aged ; Social Determinants of Health ; Pakistan ; Family ; Pacemaker, Artificial ; Social Class
    Language English
    Publishing date 2023-02-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 2192343-7
    ISSN 1744-8344 ; 1477-9072
    ISSN (online) 1744-8344
    ISSN 1477-9072
    DOI 10.1080/14779072.2023.2177636
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    Zs.A 6107: Show issues Location:
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  5. Book ; Online: PermutEx

    Khan, Muhammad Shahbaz / Ahmad, Jawad / Al-Dubai, Ahmed / Jaroucheh, Zakwan / Pitropakis, Nikolaos / Buchanan, William J.

    Feature-Extraction-Based Permutation -- A New Diffusion Scheme for Image Encryption Algorithms

    2023  

    Abstract: Traditional permutation schemes mostly focus on random scrambling of pixels, often neglecting the intrinsic image information that could enhance diffusion in image encryption algorithms. This paper introduces PermutEx, a feature-extraction-based ... ...

    Abstract Traditional permutation schemes mostly focus on random scrambling of pixels, often neglecting the intrinsic image information that could enhance diffusion in image encryption algorithms. This paper introduces PermutEx, a feature-extraction-based permutation method that utilizes inherent image features to scramble pixels effectively. Unlike random permutation schemes, PermutEx extracts the spatial frequency and local contrast features of the image and ranks each pixel based on this information, identifying which pixels are more important or information-rich based on texture and edge information. In addition, a unique permutation key is generated using the Logistic-Sine Map based on chaotic behavior. The ranked pixels are permuted in conjunction with this unique key, effectively permuting the original image into a scrambled version. Experimental results indicate that the proposed method effectively disrupts the correlation in information-rich areas within the image resulting in a correlation value of 0.000062. The effective scrambling of pixels, resulting in nearly zero correlation, makes this method suitable to be used as diffusion in image encryption algorithms.
    Keywords Computer Science - Cryptography and Security
    Subject code 006
    Publishing date 2023-11-05
    Publishing country us
    Document type Book ; Online
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  6. Article ; Online: Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques.

    Aamir, Sanam / Rahim, Aqsa / Aamir, Zain / Abbasi, Saadullah Farooq / Khan, Muhammad Shahbaz / Alhaisoni, Majed / Khan, Muhammad Attique / Khan, Khyber / Ahmad, Jawad

    Computational and mathematical methods in medicine

    2022  Volume 2022, Page(s) 5869529

    Abstract: Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various ... ...

    Abstract Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data.
    MeSH term(s) Breast ; Breast Neoplasms/diagnostic imaging ; Diagnosis, Computer-Assisted/methods ; Female ; Humans ; Neural Networks, Computer ; Support Vector Machine
    Language English
    Publishing date 2022-08-16
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2252430-7
    ISSN 1748-6718 ; 1748-670X ; 1027-3662
    ISSN (online) 1748-6718
    ISSN 1748-670X ; 1027-3662
    DOI 10.1155/2022/5869529
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    Zs.A 6373: Show issues Location:
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  8. Article ; Online: Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset.

    Umair, Muhammad / Khan, Muhammad Shahbaz / Ahmed, Fawad / Baothman, Fatmah / Alqahtani, Fehaid / Alian, Muhammad / Ahmad, Jawad

    Sensors (Basel, Switzerland)

    2021  Volume 21, Issue 17

    Abstract: The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this ...

    Abstract The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.
    MeSH term(s) Artificial Intelligence ; COVID-19 ; Deep Learning ; Humans ; SARS-CoV-2 ; X-Rays
    Language English
    Publishing date 2021-08-29
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s21175813
    Database MEDical Literature Analysis and Retrieval System OnLINE

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