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.

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.

"Who Will Marry a Diseased Girl?" Marriage, Gender, and Tuberculosis Stigma in Asia.

In a qualitative study on the stigma associated with tuberculosis (TB), involving 73 interviews and eight focus groups conducted in five sites across three countries (Bangladesh, Nepal, and Pakistan), participants spoke of TB's negative impact on the marriage prospects of women in particular. Combining the approach to discovering grounded theory with a conceptualization of causality based on a realist ontology, we developed a theory to explain the relationships between TB, gender, and marriage. The mechanism at the heart of the theory is TB's disruptiveness to the gendered roles of wife (or daughter-in-law) and mother. It is this disruptiveness that gives legitimacy to the rejection of marriage to a woman with TB. Whether or not this mechanism results in a negative impact of TB on marriage prospects depends on a range of contextual factors, providing opportunities for interventions and policies.

p27kip1 Is Required for Functionally Relevant Adult Hippocampal Neurogenesis in Mice.

We asked whether cell-cycle associated protein p27kip1 might be involved in the transition of precursor cells to postmitotic maturation in adult hippocampal neurogenesis. p27kip1 was expressed throughout the dentate gyrus with a strong nuclear expression in early postmitotic, calretinin-positive neurons and neuronally determined progenitor cells (type-3 and some type-2b), lower or absent expression in radial glia-like precursor cells (type-1) and type-2a cells and essentially no expression in granule cells. This suggested a transitory role in late proliferative and early postmitotic phases of neurogenesis. Inconsistent with a role limited to cell cycle arrest the acute stimuli, voluntary wheel running (RUN), environmental enrichment (ENR) and kainate-induced seizures increased p27kip1 expressing cells. Sequential short-term combination of RUN and ENR yielded more p27kip1 cells than either stimulus alone, indicating an additive effect. In vitro, p27kip1 was lowly expressed by proliferating precursor cells but increased upon differentiation. In p27kip1-/- mice neurogenesis was reduced in vivo, whereas the number of proliferating cells was increased. Accordingly, the microdissected dentate gyrus of p27kip1-/- mice generated more colonies in the neurosphere assay and an increased number of larger spheres with the differentiation potential unchanged. In p27kip1-/- monolayer cultures, proliferation was increased and cell cycle genes were upregulated. In the Morris water maze p27kip1-/- mice learned the task but were specifically impaired in the reversal phase explainable by the decrease in adult neurogenesis. We conclude that p27kip1 is involved in the decisive step around cell-cycle exit and plays an important role in activity-regulated and functionally relevant adult hippocampal neurogenesis. Stem Cells 2017;35:787-799.

The cyclin-dependent kinase inhibitor p27 kip1 regulates radial stem cell quiescence and neurogenesis in the adult hippocampus.

Members of the cyclin-dependent kinase (CDK)-inhibitory protein (CIP)/kinase-inhibitory protein (KIP) family of cyclin-dependent kinase inhibitors regulate proliferation and cell cycle exit of mammalian cells. In the adult brain, the CIP/KIP protein p27(kip1) has been related to the regulation of intermediate progenitor cells located in neurogenic niches. Here, we uncover a novel function of p27(kip1) in the adult hippocampus as a dual regulator of stem cell quiescence and of cell-cycle exit of immature neurons. In vivo, p27(kip1) is detected in radial stem cells expressing SOX2 and in newborn neurons of the dentate gyrus. In vitro, the Cdkn1b gene encoding p27(kip1) is transcriptionally upregulated by quiescence signals such as BMP4. The nuclear accumulation of p27(kip1) protein in adult hippocampal stem cells encompasses the BMP4-induced quiescent state and its overexpression is able to block proliferation. p27(kip1) is also expressed in immature neurons upon differentiation of adult hippocampal stem cell cultures. Loss of p27(kip1) leads to an increase in proliferation and neurogenesis in the adult dentate gyrus, which results from both a decrease in the percentage of radial stem cells that are quiescent and a delay in cell cycle exit of immature neurons. Analysis of animals carrying a disruption in the cyclin-CDK interaction domain of p27(kip1) indicates that the CDK inhibitory function of the protein is necessary to control the activity of radial stem cells. Thus, we report that p27(kip1) acts as a central player of the molecular program that keeps adult hippocampal stem cells out of the cell cycle.

Structured performance monitoring of TB-care at facility, district and province levels - Pakistan experience.

OBJECTIVE: To develop and evaluate a more structured process for effective tuberculosis control monitoring. METHODS: The quasi-experimental exploratory study was conducted from April 2007 to January 2008 in the Punjab province of Pakistan. Eight intervention districts were compared with eight control districts. Intervention consisted of managers using performance monitoring guidelines and tools for monitoring meetings at the facility and district levels. Proportion of tuberculosis suspects among outpatients, registered confirmed cases and patients' default rate were monitored. Semi-structured interviews were done to assess the experience of the participants. RESULTS: The proportion of TB suspects among outpatient attendees was significantly higher in the intervention districts (95% confidence interval 1.6-1.8%). The pre-registration default also showed difference (p=0.07). The case finding during 9 months of the intervention showed 96.3% increase compared to the 9 months of the preceding year. CONCLUSIONS: The new process was effective in improving tuberculosis case finding. The process may be used to improve tuberculosis monitoring systems and other such healthcare services.

An energy efficient simultaneous-node repositioning algorithm for mobile sensor networks.

Recently, wireless sensor network (WSN) applications have seen an increase in interest. In search and rescue, battlefield reconnaissance, and some other such applications, so that a survey of the area of interest can be made collectively, a set of mobile nodes is deployed. Keeping the network nodes connected is vital for WSNs to be effective. The provision of connectivity can be made at the time of startup and can be maintained by carefully coordinating the nodes when they move. However, if a node suddenly fails, the network could be partitioned to cause communication problems. Recently, several methods that use the relocation of nodes for connectivity restoration have been proposed. However, these methods have the tendency to not consider the potential coverage loss in some locations. This paper addresses the concerns of both connectivity and coverage in an integrated way so that this gap can be filled. A novel algorithm for simultaneous-node repositioning is introduced. In this approach, each neighbour of the failed node, one by one, moves in for a certain amount of time to take the place of the failed node, after which it returns to its original location in the network. The effectiveness of this algorithm has been verified by the simulation results.

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.

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.

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.

Delivering enhanced cardiovascular (hypertension) disease care through private health facilities in Pakistan.

BACKGROUND: Cardiovascular diseases (CVDs) are one of the leading causes of death and disability in the world. Over 80% of CVD deaths take place in low-and middle-income countries. One-third of the population aged above 40 years suffers from Hypertension (HTN) and this is largely unreported as there is no registry for CVDs. No guidelines are available for use in health care facilities, especially private health facilities where practice among GPs varies considerably. We aim to conduct a Cluster Randomized Controlled trial delivering a quality HTN-CVD care package at strengthened private health facilities as compared to current practice at private health facilities. METHODS/DESIGN: A pragmatic cluster randomized trial, with qualitative and economic studies, will be conducted in Sargodha district of Punjab, Pakistan, from January 2012 to December 2016. At least 912 hypertensives will be registered in the two arms, six clusters per arm. The proposed cluster randomized controlled trial will evaluate the effects of delivering quality HTN-CVD care, through enabled private health care facilities, to achieve better case registration, adherence and hypertension control also blood glucose and serum cholesterol control. The trial will be conducted through the doctors and paramedics at private health facilities. Main outcomes are mean difference in Systolic blood pressure among the two arms. Secondary outcomes are mean change in total serum cholesterol levels and mean change in glycaemic control achieved in the adult hypertensive patients. Individual and Cluster level analysis will be done according to intention-to-treat. DISCUSSION: Due to the high burden of disease where 1 in 3 individuals aged above 45 suffers from hypertension, topped with the fact that there is a dearth of a set of available, standardised guidelines for management, the disease is constantly on a hike in Pakistan. The government has made no effort to issue a set of guidelines adapted specifically for our population and this becomes more of a problem when managing CVD in urban population through private practitioners whose practices vary widely.If our set of context sensitive guidelines show an effectiveness in the proposed intervention districts it will be replicated in other such settings. TRIAL REGISTRATION: Current Controlled Trials ISRCTN34381594.

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.

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.

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.

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.

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.