3D printing technology and its revolutionary role in stent implementation in cardiovascular disease.

Cardiovascular disease (CVD), exemplified by coronary artery disease (CAD), is a global health concern, escalating in prevalence and burden. The etiology of CAD is intricate, involving different risk factors. CVD remains a significant cause of mortality, driving the need for innovative interventions like percutaneous coronary intervention and vascular stents. These stents aim to minimize restenosis, thrombosis, and neointimal hyperplasia while providing mechanical support. Notably, the challenges of achieving ideal stent characteristics persist. An emerging avenue to address this involves enhancing the mechanical performance of polymeric bioresorbable stents using additive manufacturing techniques And Three-dimensional (3D) printing, encompassing various manufacturing technologies, has transcended its initial concept to become a tangible reality in the medical field. The technology's evolution presents a significant opportunity for pharmaceutical and medical industries, enabling the creation of targeted drugs and swift production of medical implants. It revolutionizes medical procedures, transforming the strategies of doctors and surgeons. Patient-specific 3D-printed anatomical models are now pivotal in precision medicine and personalized treatment approaches. Despite its ongoing development, additive manufacturing in healthcare is already integrated into various medical applications, offering substantial benefits to a sector under pressure for performance and cost reduction. In this review primarily emphasizes stent technology, different types of stents, highlighting its application with some potential complications. Here we also address their benefits, potential issues, effectiveness, indications, and contraindications. In future it can potentially reduce complications and help in improving patients' outcomes. 3DP technology offers the promise to customize solutions for complex CVD conditions and help or fostering a new era of precision medicine in cardiology.

A Literature Review of Microscopic Colitis.

Although the clinical importance of microscopic colitis (MC) is highly increasing, however, the disease is still mysterious due to several challenges. Recent MC data depend mainly on doubts and uncertainties leading to misclassification. This review discussed the current knowledge gaps about MC and various controversies regarding its subtypes, pathogenesis, and management. The diagnosis of MC is based mainly on histology and immunohistopathology which can discriminate two subtypes. However, transitional forms are often associated with misclassification. The site and number of the colon biopsies have been agreed upon as at least three from each side of the colon (right and left) with a total of six. There is no credible, clear explanation for the increased incidence. The etiopathogenesis is possibly multifactorial with a high impact on the immunological background. It is proposed that MC would be the initiative of irritable bowel disease, which needs further data clarification. Although budesonide is an effective treatment in most cases, budesonide-refractory MC represents a significant clinical challenge. Therefore, immunomodulators and biologics are now well-thought to be the second-line choice for treatment.

YOLO and residual network for colorectal cancer cell detection and counting.

The HT-29 cell line, derived from human colon cancer, is valuable for biological and cancer research applications. Early detection is crucial for improving the chances of survival, and researchers are introducing new techniques for accurate cancer diagnosis. This study introduces an efficient deep learning-based method for detecting and counting colorectal cancer cells (HT-29). The colorectal cancer cell line was procured from a company. Further, the cancer cells were cultured, and a transwell experiment was conducted in the lab to collect the dataset of colorectal cancer cell images via fluorescence microscopy. Of the 566 images, 80 % were allocated to the training set, and the remaining 20 % were assigned to the testing set. The HT-29 cell detection and counting in medical images is performed by integrating YOLOv2, ResNet-50, and ResNet-18 architectures. The accuracy achieved by ResNet-18 is 98.70 % and ResNet-50 is 96.66 %. The study achieves its primary objective by focusing on detecting and quantifying congested and overlapping colorectal cancer cells within the images. This innovative work constitutes a significant development in overlapping cancer cell detection and counting, paving the way for novel advancements and opening new avenues for research and clinical applications. Researchers can extend the study by exploring variations in ResNet and YOLO architectures to optimize object detection performance. Further investigation into real-time deployment strategies will enhance the practical applicability of these models.

Genomic insights into heart health: Exploring the genetic basis of cardiovascular disease.

Cardiovascular diseases (CVDs) are considered as the leading cause of death worldwide. CVD continues to be a major cause of death and morbidity despite significant improvements in its detection and treatment. Therefore, it is strategically important to be able to precisely characterize an individual's sensitivity to certain illnesses. The discovery of genes linked to cardiovascular illnesses has benefited from linkage analysis and genome-wide association research. The last 20 years have seen significant advancements in the field of molecular genetics, particularly with the development of new tools like genome-wide association studies. In this article we explore the profound impact of genetic variations on disease development, prognosis, and therapeutic responses. And the significance of genetics in cardiovascular risk assessment and the ever-evolving realm of genetic testing, offering insights into the potential for personalized medicine in this domain. Embracing the future of cardiovascular care, the article explores the implications of pharmacogenomics for tailored treatments, the promise of emerging technologies in cardiovascular genetics and therapies, including the transformative influence of nanotechnology. Furthermore, it delves into the exciting frontiers of gene editing, such as CRISPR/Cas9, as a novel approach to combat cardiovascular diseases. And also explore the potential of stem cell therapy and regenerative medicine, providing a holistic view of the dynamic landscape of cardiovascular genomics and its transformative potential for the field of cardiovascular medicine.

Protocol for a pragmatic cluster randomised controlled trial to evaluate the effectiveness of digital health interventions in improving non-communicable disease management during the pandemic in rural Pakistan.

BACKGROUND: The COVID-19 pandemic has revealed gaps in global health systems, especially in the low- and middle-income countries (LMICs). Evidence shows that patients with non-communicable diseases (NCDs) are at higher risk of contracting COVID-19 and suffering direct and indirect health consequences. Considering the future challenges such as environmental disasters and pandemics to the LMICs health systems, digital health interventions (DHI) are well poised to strengthen health care resilience. This study aims to implement and evaluate a comprehensive package of DHIs of integrated COVID-NCD care to manage NCDs in primary care facilities in rural Pakistan. METHODS: The study is designed as a pragmatic, parallel two-arm, multi-centre, mix-methods cluster randomised controlled trial. We will randomise 30 primary care facilities in three districts of Punjab, where basic hypertension and diabetes diagnosis and treatment are provided, with a ratio of 1:1 between intervention and control. In each facility, we will recruit 50 patients who have uncontrolled hypertension. The intervention arm will receive training on an integrated COVID-NCD guideline, and will use a smartphone app-based telemedicine platform where patients can communicate with health providers and peer-supporters, along with a remote training and supervision system. Usual care will be provided in the control arm. Patients will be followed up for 10 months. Our primary indicator is systolic blood pressure measured at 10 months. A process evaluation guided by implementation science frameworks will be conducted to explore implementation questions. A cost-effectiveness evaluation will be conducted to inform future scale up in Pakistan and other LMICs. DISCUSSION: Our study is one of the first randomised controlled trials to evaluate the effectiveness of DHIs to manage NCDs to strengthen health system resilience in LMICs. We will also evaluate the implementation process and cost-effectiveness to inform future scale-up in similar resource constrained settings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier-NCT05699369.

Is Early Childhood Development Care at Public Health Facilities in Pakistan Effective? A Cluster Randomized Controlled Trial.

BACKGROUND: Significant brain development in children occurs from birth to 2 years, with environment playing an important role. Stimulation interventions are widely known to be effective in enhancing early childhood development (ECD). This study aims to assess the feasibility and effectiveness of integrating ECD care delivered by lady health visitors (LHVs) at public health facilities in rural Pakistan. METHOD: A cluster randomized controlled trial was conducted through public health facilities in 2 districts of Punjab, Pakistan. A total of 22 clusters (rural health centers and subdistrict hospitals) were randomly allocated to receive routine care (control: n=11 clusters, 406 mother-child pairs) or counseling (intervention: n=11 clusters, 398 mother-child pairs). All children aged 11-12 months without any congenital abnormality were eligible for enrollment. The intervention was delivered by the LHVs to mothers with children aged 12-24 months in 3 quarterly sessions. RESULTS: The primary outcome was the prevention of ECD delays in children aged 24 months (assessed with the Ages and Stages Questionnaire-3). Analysis was done on an intention-to-treat basis. A total of 804 mother-child pairs were registered in the study, of which 26 (3.3%) pairs were lost to follow-up at the endpoint. The proportion of children with 2 or more developmental delays was significantly less in the intervention arm (13%) as compared to the control arm (41%) at an endpoint (odds ratio=0.21; 95% confidence interval=0.11, 0.42). Children in the intervention arm also had significantly better anthropometric measurements when aged 24 months than the children in the control arm. CONCLUSION: The integrated ECD care intervention for children aged 12-24 months at public health facilities was found to be effective in enhancing ECD and reducing the proportion of children with global development delays.

Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence.

Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines a heart disease dataset and deep neural networks (DNNs) with an asynchronous learning technique. The proposed approach employs a method for asynchronously updating the parameters of DNNs and incorporates a temporally weighted aggregation technique to enhance the accuracy and convergence of the central model. To evaluate the effectiveness of the proposed AFLCP method, two datasets with various DNN architectures are tested, and the results demonstrate that the AFLCP approach outperforms the baseline method in terms of both communication cost and model accuracy.

Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks.

This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the proposed model used several advanced techniques, including meta-learning ensemble technique, transfer learning, and data augmentation. Meta-learning will optimize the model's learning process, allowing it to adapt to new and unseen datasets quickly. Transfer learning will leverage the pre-trained models such as Inception, ResNet50, and DenseNet121 to enhance the model's feature extraction ability. Data augmentation techniques will be applied to artificially generate new training images, increasing the size and diversity of the dataset. Meta ensemble learning techniques will combine the outputs of multiple CNNs, improving the model's classification accuracy. The proposed work will be investigated by pre-processing the BUSI dataset first, then training and evaluating multiple CNNs using different architectures and pre-trained models. Then, a meta-learning algorithm will be applied to optimize the learning process, and ensemble learning will be used to combine the outputs of multiple CNN. Additionally, the evaluation results indicate that the model is highly effective with high accuracy. Finally, the proposed model's performance will be compared with state-of-the-art approaches in other existing systems' accuracy, precision, recall, and F1 score.

Privacy-Aware Collaborative Learning for Skin Cancer Prediction.

Cancer, including the highly dangerous melanoma, is marked by uncontrolled cell growth and the possibility of spreading to other parts of the body. However, the conventional approach to machine learning relies on centralized training data, posing challenges for data privacy in healthcare systems driven by artificial intelligence. The collection of data from diverse sensors leads to increased computing costs, while privacy restrictions make it challenging to employ traditional machine learning methods. Researchers are currently confronted with the formidable task of developing a skin cancer prediction technique that takes privacy concerns into account while simultaneously improving accuracy. In this work, we aimed to propose a decentralized privacy-aware learning mechanism to accurately predict melanoma skin cancer. In this research we analyzed federated learning from the skin cancer database. The results from the study showed that 92% accuracy was achieved by the proposed method, which was higher than baseline algorithms.

Federated Machine Learning for Skin Lesion Diagnosis: An Asynchronous and Weighted Approach.

The accurate and timely diagnosis of skin cancer is crucial as it can be a life-threatening disease. However, the implementation of traditional machine learning algorithms in healthcare settings is faced with significant challenges due to data privacy concerns. To tackle this issue, we propose a privacy-aware machine learning approach for skin cancer detection that utilizes asynchronous federated learning and convolutional neural networks (CNNs). Our method optimizes communication rounds by dividing the CNN layers into shallow and deep layers, with the shallow layers being updated more frequently. In order to enhance the accuracy and convergence of the central model, we introduce a temporally weighted aggregation approach that takes advantage of previously trained local models. Our approach is evaluated on a skin cancer dataset, and the results show that it outperforms existing methods in terms of accuracy and communication cost. Specifically, our approach achieves a higher accuracy rate while requiring fewer communication rounds. The results suggest that our proposed method can be a promising solution for improving skin cancer diagnosis while also addressing data privacy concerns in healthcare settings.

Integrating maternal depression care at primary private clinics in low-income settings in Pakistan: A secondary analysis.

Introduction: The prevalence of depression among women in Pakistan ranges from 28% to 66%. There is a lack of structured mental healthcare provision at private primary care clinics in low-income urban settings in Pakistan. This study investigated the effectiveness and processes of a facility-based maternal depression intervention at private primary care clinics in low-income settings. Materials and methods: A mixed-methods study was conducted using secondary data from the intervention. Mothers were assessed for depression using the Patient Health Questionnaire-9 (PHQ-9). A total of 1,957 mothers (1,037 and 920 in the intervention and control arms, respectively) were retrieved for outcome measurements after 1 year of being registered. This study estimated the effectiveness of the depression intervention through cluster adjusted differences in the change in PHQ-9 scores between the baseline and the endpoint measurements for the intervention and control arms. Implementation was evaluated through emerging themes and codes from the framework analysis of 18 in-depth interview transcriptions of intervention participants. Results: Intervention mothers had a 3.06-point (95% CI: -3.46 to -2.67) reduction in their PHQ-9 score at the endpoint compared with their control counterparts. The process evaluation revealed that the integration of structured depression care was feasible at primary clinics in poor urban settings. It also revealed gaps in the public-private care linkage system and the need to improve referral systems. Conclusions: Intervening for depression care at primary care clinics can be effective in reducing maternal depression. Clinic assistants can be trained to identify and deliver key depression counseling messages. The study invites policymakers to seize an opportunity to implement a monitoring mechanism toward standard mental health care.

A Self-Decoupling Technique to Realize Dense Packing of Antenna Elements in MIMO Arrays for Wideband Sub-6 GHz Communication Systems.

A self-decoupled technique is described that enables the radiating elements in the antenna array to be densely packed for multiple-input multiple-output (MIMO) wireless communications systems. High isolation between the adjacent antenna elements is obtained by fixing the radiating elements in an orthogonal configuration with respects to each other. Current from the adjacent ports cancels their impact which results in low mutual coupling. The additional benefit of this configuration is realizing a densely packed array. The ground plane of each radiating element on the array board itself are isolated to mitigate surface wave propagations to suppress mutual coupling between the antenna elements. The radiating elements are based on a modified edge-fed circular patch antenna that includes a curved slot line and open-circuited stub to widen the array's impedance bandwidth with no impact on the antenna's footprint size. The proposed technique was verified with the design of an antenna array of matrix size 4 × 4 centered at 3.5 GHz. The array had a measured impedance bandwidth of 4 GHz from 1.5 GHz to 5.5 GHz, which corresponds to a fractional bandwidth of 114%, peak gain of 3 dBi and radiation efficiency of 84%. Its average diversity gain and envelope correlation coefficient (ECC) over its operating band are 9.6 dB and <0.016, respectively. The minimum isolation achieved between the radiating elements is better than 15 dB. The dimensions of the array are 0.4 × 0.4 × 0.039λ_g^3. The proposed array has characteristics suitable for sub-6 GHz wireless communication systems

A Fault Tolerant Surveillance System for Fire Detection and Prevention Using LoRaWAN in Smart Buildings.

In recent years, fire detection technologies have helped safeguard lives and property from hazards. Early fire warning methods, such as smoke or gas sensors, are ineffectual. Many fires have caused deaths and property damage. IoT is a fast-growing technology. It contains equipment, buildings, electrical systems, vehicles, and everyday things with computing and sensing capabilities. These objects can be managed and monitored remotely as they are connected to the Internet. In the Internet of Things concept, low-power devices like sensors and controllers are linked together using the concept of Low Power Wide Area Network (LPWAN). Long Range Wide Area Network (LoRaWAN) is an LPWAN product used on the Internet of Things (IoT). It is well suited for networks of things connected to the Internet, where terminals send a minute amount of sensor data over large distances, providing the end terminals with battery lifetimes of years. In this article, we design and implement a LoRaWAN-based system for smart building fire detection and prevention, not reliant upon Wireless Fidelity (Wi-Fi) connection. A LoRa node with a combination of sensors can detect smoke, gas, Liquefied Petroleum Gas (LPG), propane, methane, hydrogen, alcohol, temperature, and humidity. We developed the system in a real-world environment utilizing Wi-Fi Lora 32 boards. The performance is evaluated considering the response time and overall network delay. The tests are carried out in different lengths (0-600 m) and heights above the ground (0-2 m) in an open environment and indoor (1st Floor-3rd floor) environment. We observed that the proposed system outperformed in sensing and data transfer from sensing nodes to the controller boards.

Bandwidth Enhancement and Generation of CP of Yagi-Uda-Shape Feed on a Rectangular DRA for 5G Applications.

A wideband circularly polarized rectangular dielectric resonator antenna (DRA) fed by a single feeding mechanism has been studied theoretically and experimentally. The purpose of the study is to determine how adding a parasitic strip next to the flat surface metallic feed would affect various far- and near-field antenna characteristics. Initially, the basic antenna design, i.e., the T-shape feed known as antenna A, produced a 4.81% impedance matching bandwidth (|S11| -10 dB). Due to the narrow and undesirable results of the initial antenna design, antenna-A was updated to the antenna-B design, i.e., Yagi-Uda. The antenna-B produced a decent result (7.89% S11) as compared to antenna-A but still needed the bandwidth widened, for this, a parasitic patch was introduced next to the Yagi-Uda antenna on the rectangular DRA at an optimized location to further improve the results. This arrangement produced circular polarization (CP) waves spanning a broad bandwidth of 28.21% (3.59-3.44 GHz) and a broad impedance |S11| bandwidth of around 29.74% (3.71-3.62 GHz). These findings show that, in addition to producing CP, parasite patches also cause the return loss to rise by a factor of almost three times when compared to results obtained with the Yagi-Uda-shape feed alone. Computer simulation technology was used for the simulation (CST-2017). The planned antenna geometry prototype was fabricated and measured. Performance indicators show that the suggested antenna is a good fit for 5G applications. The simulated outcomes and measurements match up reasonably.

Mutual Coupling Reduction through Defected Ground Structure in Circularly Polarized, Dielectric Resonator-Based MIMO Antennas for Sub-6 GHz 5G Applications.

This paper describes a singly-fed circularly polarized rectangular dielectric resonator antenna (RDRA) for MIMO and 5G Sub 6 GHz applications. Circular polarization was achieved for both ports using a novel-shaped conformal metal strip. To improve the isolation between the radiators, a "S" shaped defective ground plane structure (DGPS) was used. In order to authenticate the estimated findings, a prototype of the suggested radiator was built and tested experimentally. Over the desired band, i.e., 3.57-4.48 GHz, a fractional impedance bandwidth of roughly 36.63 percent (-10 dB as reference) was reached. Parallel axial ratio bandwidth of 28.33 percent is achieved, which is in conjunction with impedance matching bandwidth. Between the ports, isolation of -28 dB is achieved Gain and other far-field parameters are also calculated and found to be within their optimum limits.

High Performance Eight-Port Dual-Band MIMO Antenna System for 5G Devices.

This study provides an eight-component multiple-input multiple-output (MIMO) antenna architecture for fifth-generation (5G) mobile communication systems. The single antenna element is comprised of an L-shaped radiating component, an L-shaped parasitic element, and a ground plane with a rectangular slot. The main element with a slot-loaded ground plane helps to draw current from a coaxial feed from the other side of the board, while the parasitic element helps to elongate the current path and improve the impedance of the system. This enables the system to radiate at two different frequency ranges: 3.34-3.7 GHz and 4.67-5.08 GHz, with 360 MHz and 410 MHz bandwidths, respectively. For MIMO configuration, the radiating elements are designed on either side of a 0.8 mm thick FR-4 substrate, allowing space to accommodate a battery, radio frequency (RF) systems and subsystems, and camera and sensor modules. The corner and the middle elements are arranged in such a manner so that they can provide spatial and pattern diversity. Furthermore, at least 12 dB of isolation is established between any two radiating elements. Various MIMO performance parameters were evaluated, e.g., mean effective gain (MEG), channel capacity (CC), envelope correlation coefficient (ECC), realized gain, far-field characteristics, and efficiency. Single- and double-hand mode evaluations were performed to further demonstrate the capability of the proposed MIMO antenna. A prototype of the proposed MIMO antenna was manufactured and assessed to verify the simulated data. The measured and simulated results were found to be in good agreement. On the basis of its performance characteristics, the designed MIMO system could be used in 5G communication systems.

Process evaluation of chest camps for increased tuberculosis case finding in Punjab, Pakistan.

BACKGROUND: To contribute to the World Health Organization's End TBStrategy, the active tuberculosis (TB) case-finding approach has been proven effective. METHODS: A total of 66 chest camps were organised for patients in 15 selected districts in Punjab, Pakistan, in 2017. A mixed-method process evaluation was conducted in four randomly selected districts to evaluate the use of chest camps for active TB case finding to reach the maximum number of people with TB and to assess the implementation outcomes, such as effectiveness, feasibility, fidelity, and costs. RESULTS: Results indicated that 1458 attendees visited 24 chest camps in four selected districts. Among attendees, 297 presumptive cases were found and smear-tested; and 34 of the smear-tested were diagnosed as smear-positive TB patients. The prevalence of smear-positive TB patients among the chest camp participants was found to be 2.3%. The findings from interviews showed that preparation of chest camp activities, especially the involvement of community leaders, was found to be effective in achieving the desired level of attendance. The respondents found attending the chest camps for TB symptoms feasible and acceptable. The chest camp costs approximately US$280, including the pre-camp mobilisation events, whereas the cost per TB-positive patient was found to be US$197.64. CONCLUSIONS: The higher number of attendees without TB symptoms, the low proportion of smear-negative case registrations; and relatively high unit cost (per patient detected) were the areas identified for further attention. The study supports the continuation of chest camp activity, with further attention required for quality and efficiency concerns.

Effectiveness of an Integrated Care Package for Refugee Mothers and Children: Protocol for a Cluster Randomized Controlled Trial.

BACKGROUND: Thousands of Rohingya refugee mothers at the world's largest refugee camp located in Bangladesh are at risk of poor mental health. Accordingly, their children are also vulnerable to delayed cognitive and physical development. OBJECTIVE: The aim of this study is to evaluate the effectiveness of an integrated care package in reducing the prevalence of developmental delays among children aged 1 year and improving their mothers' mental health status. METHODS: This is a parallel, two-arm, single-blind, cluster randomized controlled trial (cRCT). A total of 704 mother-child dyads residing at the Kutupalong refugee camp in Cox's Bazar, Bangladesh, will be recruited from 22 clusters with 32 mother-child dyads per cluster. In the intervention arm, an integrated early childhood development and maternal mental health package will be delivered every quarter to mothers of newborns by trained community health workers until the child is 1 year old. Our primary outcome is a reduction in the prevalence of two or more childhood developmental delays of infants aged 1 year compared to the usual treatment. The secondary outcomes include reduced stunting among children and the prevalence of maternal depression. We will also assess the cost-effectiveness of the integrated intervention, and will further explore the intervention's acceptability and feasibility. RESULTS: At the time of submission, the study was at the stage of endpoint assessment. The data analysis started in December 2020, and the results are expected to be published after the first quarter of 2021. CONCLUSIONS: This study will address the burden of childhood developmental delays and poor maternal mental health in a low-resource setting. If proven effective, the delivery of the intervention through community health workers will ensure the proposed intervention's sustainability. TRIAL REGISTRATION: ISRCTN Registry ISRCTN10892553; https://www.isrctn.com/ISRCTN10892553. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25047.

Antimicrobial and synergistic activity of thiazoline derivatives in combination with conventional antibiotics against multidrug resistant Staphylococcus aureus isolated from abscess drainage samples.

Emergence and spread of multidrug resistant (MDR) Staphylococcus aureus strains is becoming major challenge in treatment of soft tissue infections. This study aimed to explore antimicrobial and synergistic antimicrobial potential of three commercially available thiazoline derivatives (2-amino-2-thiazoline, 2-thiazoline-2-thiol and 2-acetyl-2-thiazoline) against MDR Staphylococcus aureus strains isolated from abscess drainage samples (n=20). MDR Staphylococcus aureus isolates were identified by Kirby-Bauer disk diffusion assay and were further subjected to molecular identification by 16srRNA amplification and DNA sequencing. Minimum Inhibitory Concentration (MIC) values of test compounds and antibiotics (0.25-512µg/mL) were measured and subsequently, synergism assay was performed to calculate Fractional Inhibitory Concentration (FIC) index. Out of twenty Staphylococcus aureus isolates, sixteen (80%) were found to be MDR whereas four (20%) were Non-MDR. Moxifloxacin and vancomycine were found most effective antibiotics, inhibiting 100% (n=20) and 95% (n=19) strains respectively. Antimicrobial activity of 2-amino-2-thiazoline (MIC: 32µg/mL), 2-thiazoline-2-thiol (MIC: 64µg/mL) and 2-acetyl-2-thiazoline (MIC: 32µg/mL) was found significant against all ten tested MDR strains. Synergistic combinations of thiazoline derivatives with test antibiotics reduced MIC values significantly. Therefore, combination of tested thiazoline derivatives with antibiotics could be used as alternative therapeutic approach to treat soft tissue infections caused by MDR Staphylococcus aureus after further pre-clinical and clinical studies.

Enhanced hypertension care through private clinics in Pakistan: a cluster randomised trial.

BACKGROUND: Hypertension in Pakistan affects 33% of people aged ≥45 years, and in urban areas around 70% of basic health care occurs in private facilities. AIM: To assess whether enhanced care at urban private clinics resulted in better control of hypertension, cardiovascular disease (CVD) risk factors, and treatment adherence. DESIGN & SETTING: A two-arm cluster randomised controlled trial was conducted at 26 private clinics (in three districts of Punjab) between January 2015-September 2016. Both arms had enhanced screening and diagnosis of hypertension and related conditions, and patient recording processes. Intervention facilities also had a clinical care guide, additional drugs for hypertension, a patient lifestyle education flipchart, associated training, and mobile phone follow-up. METHOD: Clinics were randomised in a 1:1 ratio (sealed envelope lottery method). A total of 574 intervention and 564 control patients in 13 clusters in each arm were recruited (male and female, aged ≥25 years, systolic blood pressure [SBP] >140 mmHg, and/or diastolic blood pressure [DBP] >90 mmHg). The primary outcome was change in SBP from baseline to 9-month follow-up.Staff and patients were not blinded, but outcome assessors were blinded. RESULTS: Nine-month primary outcomes were available for 522/574 (90.9%) intervention and 484/564 (85.8%) control participants (all clusters). The unadjusted cluster-level analysis results were as follows: mean intervention outcome was -25.2 mmHg (95% confidence intervals [CI] = -29.9 to-20.6); mean control outcome was -9.4 mmHg (95% CI = 21.2 to 2.2); and mean control-intervention difference was 15.8 (95% CI = 3.6 to 28.0; P = 0.01). CONCLUSION: The findings and separate process evaluation support the scaling of an integrated CVD-hypertension care intervention in urban private clinics in areas lacking public primary care in Pakistan.