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Chronic Hepatitis C Virus (HCV) infection is still a major health issue especially in endemic areas where fewer direct-acting virals (DAAs) are treatment options. Some HCV variants are associated with resistance and it reduces DAAs success where pre-existing variants prevail. In this study, we investigated resistance-associated polymorphisms (RAPs) in the HCV NS3 region from DAAs naïve Pakistani patients. 277 chronic HCV treatment naïve patients infected with genotype 1a, 3a and 3b were selected from various clinical centers in the capital city of Khyber Pakhtunkhwa province Pakistan. All the patients were included in this study after taking informed consent. HCV NS3 region was amplified and Sanger sequencing was performed to analyze RAPs to NS3 protease inhibitors. Of the total 29.24% (81/277) patients had detected with known RAPs viz V36A/G/L, T54S, V55A/D/I, Q80K/R, S122G/T/R, R155K/T/I, V158I, D168T/Q, and I170V. Among HCV-1a subjects overall RAPs found were 26.09% (12/46) and most prevalent substitutions were V36A/G (10.87%, 5/46) and R155K/T/I (8.70%, 4/46). Of the total HCV-3a infected patients, 30.95% were observed with RAPS. Ammon these, the most frequent substitutions were Q80R (13.69%, 23/168) followed by V36L (18.33%, 14/168) and V55I (5.95%, 10/168). Among HCV-3b patients, 26.98% were found with RAPs and S122R and Q80R were the dominant variants detected in 17.46 (11/63) and 12.70% (8/63) patients respectively. All these substitutions were associated with Boceprevir, Simeprevir, Telaprevir, and Paritaprevir. Single substitution in one sequence was found in 18.77% (52/277) and multiple in 10.46% (29/277). More than one RAP was frequent in HCV-3a sequences. Natural RAPs are common in chronic HCV patients infected with genotype 1a, 3a and 3b, the most prevalent subtypes in Pakistan. High prevalence of HCV NS3 RAPs suggested a large scale study of the NS3 gene before the introduction of NS3 protease inhibitors in Pakistan.

Periodic monitoring of physiological data of inward patients' is a way to keep their health conditions under control. For this purpose, the medical staff record patients' vital signs periodically. Such an approach is exhaustive and may not be safe for patients because in some cases, a slight negligence from health care staff may lead to serious consequences for the patients. This paper suggests a MPI cluster based distributed computing framework in which newly surfaced low cost single board computers called Raspberry Pi is used for continuous monitoring of in-ward patients' vital sign data and sending them to the hospital server for doctors' observation and data logging. Alert signals may be triggered in case of any vital sign data exceeding some predefined dangerous limits. Furthermore, the hospital server can be accessed through Internet for remote monitoring of real time physiological data.

In emergencies or life-threatening situations, patients are generally shifted to hospitals in ambulances. The health conditions of on-board patients can become critical if they are not evaluated and treated in time. Chances of saving lives can increase significantly if patients’ vital signs inside an ambulance or on-site triage area are transferred to a hospital in real time. If the ambulances are linked to target hospitals, then the physicians in emergency rooms can monitor on-board patients’ vital signs and issue instructions to paramedics to stabilize patients’ medical conditions before they reach the assigned hospitals. Transferred vital signs data may also be archived for medical records. The Internet of things (IoT) is a paradigm which envisions Internet connectivity of virtually everything on the earth. In this paper, an IoT-based low-cost solution is proposed to monitor, archive, analyze, and tag the vital signs data of multiple patients and transfer them to the remote hospital in real time. This opens up a lot of possibilities in telemedicine and disaster management. As a proof of concept, the functionality of the proposed system was validated by developing a prototype model utilizing an IoT-enabled medical sensor board and a Linux server mimicking the remote hospital server. Results of actual data transmission obtained during experimentation are also provided. It is hoped that the proposed system can play a role in saving human lives in disaster situations.