Hematochezia: An Uncommon Presentation of Colonic Tuberculosis

Abdominal tuberculosis (TB) is an uncommon entity in the United States. Colonic TB is reported in 2-3% of patients with abdominal TB. It is frequently misdiagnosed as Crohn’s disease or carcinoma of the colon due to their shared clinical, radiographic, and endoscopic presentations. We present a case of a 72-year-old male with colonic tuberculosis presenting as hematochezia. Our patient presented with shortness of breath and weight loss. Chest X-ray demonstrated ill-defined bilateral parenchymal opacities in the perihilar, mid, and lower lung zones. The patient was diagnosed and treated for community acquired pneumonia, with no improvement. Hematochezia complicated by symptomatic hypotension developed later in the course of admission. Colonoscopy revealed multiple ulcers at the anus and transverse and ascending colon as well as the cecum with stigmata of bleeding. Biopsy of a sigmoid ulcer was consistent with colonic tuberculosis. Antitubercular therapy was initiated, but the patient passed away secondary to multiorgan failure 29 days into admission

1-Ammonio­naphthalene-2-sulfonate

In the mol­ecule of the zwitterionic title compound, C10H9NO3S, an intra­molecular N—H⋯O hydrogen bond results in the formation of an almost planar six-membered ring (r.m.s daviation = 0.0150 Å), which is oriented at a dihedral angle of 1.63 (3)° with respect to the naphthalene ring system. In the crystal structure, inter­molecular N—H⋯O hydrogen bonds link the mol­ecules into a two-dimensional network

Vagus Nerve Acupucture-Like Transcutanious Electrical Nerve Stimulation on Immunity After Liver Resection

Objective: To find out the therapeutic impact of acupuncture like-transcutaneous electrical nerve stimulation of vagus nerve on immunity after liver resection. Methods This was a single-blind randomized controlled trial. A total of sixty individuals who had undergone liver resection at the National Liver Institute Hospital at Menofiea University were randomly divided into two groups: study group A (n=30) and control group B (n=30). The study group had vagus nerve stimulation with acupuncture like-TENS parameters include low-frequency (2–10Hz), pulse width (100–40

Machine Learning-driven Optimization for Intrusion Detection in Smart Vehicular Networks

An essential element in the smart city vision is providing safe and secure journeys via intelligent vehicles and smart roads. Vehicular ad hoc networks (VANETs) have played a significant role in enhancing road safety where vehicles can share road information conditions. However, VANETs share the same security concerns of legacy ad hoc networks. Unlike exiting works, we consider, in this paper, detection a common attack where nodes modify safety message or drop them. Unfortunately, detecting such a type of intrusion is a challenging problem since some packets may be lost or dropped in normal VANET due to congestion without malicious action. To mitigate these concerns, this paper presents a novel scheme for minimizing the invalidity ratio of VANET packets transmissions. In order to detect unusual traffic, the proposed scheme combines evidences from current as well as past behaviour to evaluate the trustworthiness of both data and nodes. A new intrusion detection scheme is accomplished through a four phases, namely, rule-based security filter, Dempster–Shafer adder, node’s history database, and Bayesian learner. The suspicion level of each incoming data is determined based on the extent of its deviation from data reported from trustworthy nodes. Dempster–Shafer’s theory is used to combine multiple evidences and Bayesian learner is adopted to classify each event in VANET into well-behaved or misbehaving event. The proposed solution is validated through extensive simulations. The results confirm that the fusion of different evidences has a significant positive impact on the performance of the security scheme compared to other counterparts

A Lightweight and Efficient Digital Image Encryption Using Hybrid Chaotic Systems for Wireless Network Applications

Due to limited processing capabilities and other constraints of most wireless networks, many existing security algorithms do not consider the network efficiency. This is because most of these security solutions exhibit intolerable overhead and consider only securing scalar data, which are not suitable for other data types such as digital images, hence affecting the provided security level and network performance. Thus, in this paper, we propose a lightweight and efficient security scheme based on chaotic algorithms to efficiently encrypt digital images. Our proposed algorithm handles digital images in two phases: Firstly, digital images are split into blocks and compressed by processing them in frequency domain instead of Red-Green-Blue (RGB) domain. The ultimate goal is to reduce their sizes to speed up the encryption process and to break the correlation among image pixel values. Secondly, 2D Logistic chaotic map is deployed in key generation, permutation, and substitution stages for image pixel shuffling and transposition. In addition, 2D Henon chaotic map is deployed to change the pixel values in the diffusion stage in order to enhance the required level of security and resist various security attacks. Security performance analysis based on standard test images shows that our proposed scheme overcomes the performance of other existing techniques

A multidisciplinary approach to triage patients with breast disease during the COVID-19 pandemic: Experience from a tertiary care center in the developing world

Background: The COVID-19 pandemic has created a need to prioritize care because of limitation of resources. Owing to the heterogeneity and high prevalence of breast cancers, the need to prioritize care in this vulnerable population is essential. While various medical societies have published recommendations to manage breast disease during the COVID-19 pandemic, most are focused on the Western world and do not necessarily address the challenges of a resource-limited setting.Aim: In this article, we describe our institutional approach for prioritizing care for patients presenting with breast disease.Methods and results: The breast disease management guidelines were developed and approved with the expertise of the Multidisciplinary Breast Program Leadership Committee (BPLC) of the Aga Khan University, Karachi, Pakistan. These guidelines were inspired, adapted, and modified keeping in view the needs of our resource-limited healthcare system. These recommendations are also congruent with the ethical guidelines developed by the Center of Biomedical Ethics and Culture (CBEC) at the Sindh Institute of Urology and Transplantation (SIUT), Karachi. Our institutional recommendations outline a framework to triage patients based on the urgency of care, scheduling conflicts, and tumor board recommendations, optimizing healthcare workers\u27 schedules, operating room reallocation, and protocols. We also describe the Virtual Blended Clinics , a resource-friendly means of conducting virtual clinics and a comprehensive plan for transitioning back into the post-COVID routine.Conclusion: Our institutional experience may be considered as a guide during the COVID-19 pandemic, particularly for triaging care in a resource-limited setting; however, these are not meant to be universally applicable, and individual cases must be tailored based on physicians\u27 clinical judgment to provide the best quality care